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You
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You
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We'll start in a minute or so.

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Yeah, so we are ready to get started.
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Yeah, so we are ready to get started.

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So if you guys in a place to switch on the camera, please do.

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We'd love to see some faces.

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That's only asked that I have time.

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I would love to understand who's in the room.

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What's your background?

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What do you do?

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What's your interest in party club engineering?

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And then again, you guys are playing for roles.
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And then again, you guys are playing for roles.

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Like what's your general of black questions you may have.

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Also, feel free to share your.

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LinkedIn as well in the chat so that others can join us as well.

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Hey, so how are we doing?

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Thank you everyone for introducing yourself, but yeah.
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Thank you everyone for introducing yourself, but yeah.

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Yeah, if you can rename yourself as well, so others can know who's in the room.

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That's helpful.

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Thank you.

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Thank you.
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Thank you.

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Yeah, and as an engineer, feel free to share LinkedIn so others can connect.

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So I'll jump into it.

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So these sessions are generally like discussions at the end of the day.

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I do have a slide deck, but I would love to hear your thoughts as well because

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I'm good.

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Like you guys to ask questions to make the best of the session.

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So especially if you guys are playing for jobs and then like looking too.

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After you roles like what that looks like would love to hear that.
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After you roles like what that looks like would love to hear that.

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Yeah, so, I mean, the term is quite popular these days.

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I thought it was engineering.

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So I'm going to try like breaking down as much as possible to sort of like.

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Explain what everything means and then like what it looks like in different

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places because different firms would have like different variations of the role

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.

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And then like how the interview process looks like but sort of skills you need

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to have.

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All right, so the focus today is going to be sort of like looks at a bit about

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why.
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why.

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Sort of this general gen AI sort of solutions fail or in the world.

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So you might have seen a lot of reports about it and going to some sort of like

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articles about this are some statistics around this.

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And then we'll look into like what an FTE is like what's a vertical engineer

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and then sort of like going to like what they do, like pushing things into

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messy systems and then how do you sort of like deal with like actual clients.

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Because there's a client facing component to it, not just the engineering side.

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And then how do you like crack the interview for this, which is quite.
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And then how do you like crack the interview for this, which is quite.

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I think like is adjacent to like engineering roles, but it has a bit more

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nuance to that compared to just normal engineering roles and then at the end of

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Q&A as well.

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All right, so you might have seen this, there's like MIT sort of published

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article about like this.

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It's like 95% of gen AI deployments, like this is relatively old like last year

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, didn't like 95% of them sort of delivered zero measurable sort of impact.
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, didn't like 95% of them sort of delivered zero measurable sort of impact.

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So it was quite alarming, given that everybody was on the AI set like bandwagon

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. And then again, like when you look at the results, like there's a lot of

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mistrust and dissatisfaction on places where these things not get enrolled out.

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So this is a common situation like last year, and to address this is one of the

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main reasons AIFD roles that like came about. And yeah, so if you look at it,
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main reasons AIFD roles that like came about. And yeah, so if you look at it,

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like these are some of the other reports that I found out as well.

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So, I found out as well, so garden, I said like 40% of the, um, agent to get

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projects will be canceled by 27, 30% of these BCG, like 3% like AI, effortless

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people and process non algorithms.

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So the idea is that like you need to like sort of bring in a lot of these

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people who are currently working in these places like to actually into the mix.

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And then, so Mackenzie report is like Mackenzie finds that 8% from CCA, but

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only about 6% are high performance. So, so there's something like the stats
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only about 6% are high performance. So, so there's something like the stats

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about it.

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So, and then like to address some of these issues are the AI for deployed

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engineer role is created.

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So just like another staff, like 2% of the externally part part of the tools

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succeeded. The reason is that rather than you just go and like selling a

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solution, like you need to like set of like work.

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With that team to implement that your product within that team.

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There's also like debate around this whether this is a good approach, getting
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There's also like debate around this whether this is a good approach, getting

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to that at the end because there's like a lot of mistrust around like players

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that can't open AI trying to like get IP from different clients.

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There's a whole other debate, but when the market is going, this role is

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currently at least like from a hiring point of view, make a lot of sense, a lot

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of people are hiring.

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And I'll show some of these acquisitions that sort of like sort of like talk to

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that.

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So let's look at like what FDE is right 40 per engineer, and that's the main
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So let's look at like what FDE is right 40 per engineer, and that's the main

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thing we're trying to make sense.

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So the term sort of came from Palantir. They had this function called deltas.

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They focused on just one customer.

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They go and sort of like help them implement the solution and this will expand

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from there.

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And it's comes from these like four deployed units in military because Palantir

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being like consultancy slash sort of like service provider for defense tech.

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What they sort of like had this role is that they had a few engineers like who
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What they sort of like had this role is that they had a few engineers like who

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goes and work in the actual field, like in the battlefield, trying to like test

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this software that they build.

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So this is where the name comes in.

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Now, so everybody sort of like sort of like took that and implement within the

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asset open and it has like FDEs and that Google has FDEs.

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Everybody has FDEs. So it's not like necessarily the actual battlefield, but

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you had client science trying to like implement what you'll have.

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So pretty much it is very similar to like a startup CTO role where you have to
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So pretty much it is very similar to like a startup CTO role where you have to

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like actually deal with a lot of problems and then like come up with solutions

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as you go.

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That this is the starting point. So, but it's good to like position this like

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with other types of roles.

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So you have like sales or solutions in general is a consultant roles, generally

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it might consider consultants, and then there's like product engineers.

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So, sales solution engineers, they do demos and POCs, but these solutions would

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not necessarily go into the client's application side where consultants may not

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most of the time build products.
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most of the time build products.

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I mean, still make build POCs right now, but it's pretty much as advisory,

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giving a roadmap on how to do things.

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Where co-product engineers is pretty much like you focus on working in your

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product.

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For example, if let's say open AI and Tropic has product engineers, they focus

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on the model side of things.

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They work internally to improve that where FDEs, the idea is that you actually

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go and like work with your client and try and implement that within their

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system.

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And the code that you write is actually production-ready code.

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So you're not just like building demos, you're actually working with that team,
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So you're not just like building demos, you're actually working with that team,

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you sit within that team to like implement that.

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So that's the general, I would say, 90% of the roles would actually fit into

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this definition.

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So it's good to understand this so that you sort of understand what you're

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getting into.

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So this is like another example that I saw, like for example.

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So these are two acquisitions that happened, like so open AI sort of acquired a

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company called Tomorrow.

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This one, now it's called DeployCo, which is they have like a bunch of FDEs,
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This one, now it's called DeployCo, which is they have like a bunch of FDEs,

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this is high like fast-growing startup in the UK.

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And they sort of acquired them to be the external like this for deployed arm.

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Similarly, at the same time, and Tropic and Blackstone launch ODE, which is 1.5

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billion implementation partner to implement and Tropic within different clients

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.

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So this is like early like a few months back.

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But this is just an indication that a lot of these big players, like even
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But this is just an indication that a lot of these big players, like even

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Google does it right now, are pushing to acquire for the engineers because it's

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a very unique skill set.

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For example, DeployCo, they had like the predominantly focused on opening and

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deployment.

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So this is health, it was an easy acquisition play for open AI because the

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people like who worked in that team are pretty much experts on opening and

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ecosystem.

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So it's a no-brainer for them to acquire likewise for the ODE as well.

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So the idea is simple, you get a bunch of people who are experts in the

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products that they're selling and also have consultancy experience, you sort of
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products that they're selling and also have consultancy experience, you sort of

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like get them to go and like do the implementation for you.

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All right, so if you look at the demand, I think like you might have already

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seen this.

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Like a 700% growth on this. And then, and recently it says the hottest job in

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startups these days, obviously these are like take these things with the grain

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of souls because obviously people like to hype it up.

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But looking at the hiring at least, this is getting like quite popular.

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The main reason is that deploying AI out in the wild is quite hard.
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The main reason is that deploying AI out in the wild is quite hard.

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So you need like not just like just one of implementations, you actually need

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somebody with the right expertise to like work with them for a very long period

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of time.

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Like at least six months, so that you can make sure these interventions work.

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The models work and the right, the adoption works really well.

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This helps both sides because if the integration works, it's good for the model

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providers that we can have a client for a long period of time.

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And it's good for the client as well because you have an expert from the

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specific team to actually help you figure out like what sort of work first to
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specific team to actually help you figure out like what sort of work first to

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implement.

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So that's the main reason behind this being quite popular, especially in the AI

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center implementation side.

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So, one of the things like you will probably need to like get used to is like

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shipping into my systems because you're going into like a totally different

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ecosystem for what you're used to the already systems running.

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Already workflows that going like these are like ad hoc excel sheets like

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different kinds of things you're looking at.

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So you need to like make sure that you're quite comfortable with that.
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So you need to like make sure that you're quite comfortable with that.

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So this would be like a general process that you may do, for example, you spend

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a lot of time scoping, like understand the requirement, talking to clients.

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So you sort of like go and work with the team generally in person or you

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remotely, you sort of like sit with them to talk to that.

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I'll say like you probably spend like 60, five to 70%, maybe on this, like you

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actually like spend a lot of time trying to figure out like what needs to get

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built and you scope it out really well.
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built and you scope it out really well.

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And then on top of that, you define the validation layer as well like how do

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you like make sure what success looks like.

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So these could be emails could be like system checks like whatever that is like

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you sort of like define that as a lot of front.

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So the problem, the validation is defined then after that you go and deliver

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that so that so that so more time you spend on this planning space stage one

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and two, the delivery becomes much more easier for us to.

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So that's where like the general after roles that I've seen work out in the
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So that's where like the general after roles that I've seen work out in the

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world.

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Yeah, so I think like if you break it down a bit more, this is some of the sort

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of the best practices I've seen, like you generally put emails before model so

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that's why I like things are quite important like you make sure that you define

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success criteria and based on that

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what emails looks like and then you implement that generally rather than trying

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to sort of absorb this massive scope, you try and like build relatively small

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scope like get one workflow one sort of like this process automated.
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scope like get one workflow one sort of like this process automated.

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It's much more better to have like a reliable repeatable automation rather than

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this build out like massive transformation because it's very hard to like

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maintain just in one goes generally layer by layer you sort of build it out.

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And also like you generally would have human in the loop so that means like

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what are they generate you're trying to like put as much as possible touch

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points to get approval from human so that you can actually look at these

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results here human in the
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results here human in the

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systems, like could be domain experts engineers themselves, so that you can

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like make sure things work as expected. And then you kind of like focused on a

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lot of like logging and monitoring you put the safety net first what that means

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is like you put the right kind of

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of space except try to see like if things are working sort of ties back to us

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as well to a certain extent you're looking at up you have to like make sure
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as well to a certain extent you're looking at up you have to like make sure

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like have a good sense of how the systems work.

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And then you have a budget for on glamorous work what that means is like there

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's a lot of like data engineering work you're like talk to people like back and

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forth like because.

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I do a lot of these work, and like when we go to like different organizations I

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have to like work with different teams. So they are it team department might

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have like a very specific way of working sort of understanding that and sort of

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making sure those things are aligned

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is takes a bit of time so it's very important that you budget for that as well
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is takes a bit of time so it's very important that you budget for that as well

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because there's time commitment that needs to go into making sure things are

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set in that way. It's not just building a models like handling this as well.

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You know this really well.

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All right, so you also need to like make sure that you plan for to deliver

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something so like mid market sort of like firms you're looking at 90 days to

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production, larger enterprises up to nine months and then Sierra and I go on

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these.

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All these are like sort of like implementers they've sort of like mentioned
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All these are like sort of like implementers they've sort of like mentioned

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like two weeks to two months for implementation where they go on says like six

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weeks to from this card to deploy.

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So it depends on the firm as well, but if you're looking at mid market like

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large enterprise, you're looking beyond like three months or even six months to

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deliver something so it's a long process even to deliver something small, but

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when the first one works.

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It's a relatively easier increment to add future results.

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So the next part is like earning trust on the ground. So you can't just like be
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So the next part is like earning trust on the ground. So you can't just like be

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an engineer just go and ask them to build stuff you have to like work with the

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executives you need to work with the rest of the team to try and sort of

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understand what they want what that needs.

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So you pretty much, even though you're hired from let's say opening your Google

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, you are pretty much in this period, you should act as an employee for that

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company you need to understand their teams you need to actually work within

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that team to like make sure you get

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like with their trust and start implementing towards the KPIs that they care

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about.

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So it's important you have the right communication skills right kind of like
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So it's important you have the right communication skills right kind of like

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sort of like interpersonal skills like get these things working.

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So there's all the, I would say, four habits that I've seen that's going to

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like help you with this process.

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So be physical there as much as possible. If there's a possibility to like be

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on side, it's much more easy to consume information.

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If you look at like 40 per engineers in volunteer, like you actually go to vote

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like you were staying.

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I mean, not like frontline, but at least physically be there.

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So then you start demoing small and then like get a lot of feedbacks rather
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So then you start demoing small and then like get a lot of feedbacks rather

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than waiting to build like an entire process and wait until end and the end to

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actually see something so fast iterations and also like other things like

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saying no.

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So generally you get something I struggle with that as well like I say yes to

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everything, but it's not going to be practical if you say yes to everything.

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So because so there's a question panty, panty for deployed engineers, that's

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what if the means.
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what if the means.

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So you're looking at scoping things like small as possible.

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So in that way, they're looking at delivering something meaningful rather than

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trying to absorb and solve everything.

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It's not going to work.

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And whenever something goes wrong, like sure that rather than trying to hide it

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, because rather waiting until then like we need to like define like what sort

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of issues your system have their systems have, and like what sort of

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limitations that the
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limitations that the

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integration might have like generally like try not to oversell.

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That's what I'm trying to say because you need to like be open about like what

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let's say if you're opening a 40 per engineer, what sort of limitations the

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model says things are hallucinations like the model capacity etc.

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All of these things needs to be sort of handled as much as possible.

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There's some like examples on the left hand side, like fortune 500 insurer

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company ran sanctioned pilot.

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It was fancy. So I put the links to this at the end. So this MIT sort of like
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It was fancy. So I put the links to this at the end. So this MIT sort of like

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study sort of sort of found this, where at the end of the day, the integration

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that didn't work, and people started using their own sort of tools.

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So that's like one good example of this.

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AID plan is not working really well. And second one is that Morgan Stanley with

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OpenAI. So they had this like for the plane engineer concept. They have like

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people working within their teams actually deploy this and that sort of

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resulted in like a really much better adoption.

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So I think like that's a good results for both sides, because there's time
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So I think like that's a good results for both sides, because there's time

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saving for Morgan Stanley and open a and they got a long term client for it. So

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some of the examples that came up from that research I would recommend reading

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that.

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So there's another one from OpenAI. So, OpenAI like actually went to the farm.

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And then they had to work with John Deere to actually try and like implement

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this was a really interesting case study as well, because when you work with

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the team, you actually see the problems rather than like doing things in

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abstract.
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abstract.

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Generally, that's my recommendation as well, if you're doing consultant to work

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, this is like, but I'm better for you. And if the is very similar.

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All right, so something like some myths around this.

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It's not a rebranded as sales engineer, you actually write code that will

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actually work on the client side.

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It's consulting, it is not consultant generally recommend, not necessarily

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shape, even if you ship, it might not be integrated to the core product, you

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actually build things to the product itself.

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The hard part is a model. It is not the case. It is around the process and
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The hard part is a model. It is not the case. It is around the process and

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everything around that and understanding that building things are model is like

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, let's say 20% of the problem, 80% like 70% is rest of the sort of ecosystem

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around that.

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You don't need a PhD. You just need to like have this like CTO mindset, who's

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like quite capable of with high agency, you sort of solve problems.

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PhD is good to like the model part, but the rest of it's pretty much around

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building things much faster, working with teams and sort of like making sure

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things deploy properly and you over communicate as much as possible.
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things deploy properly and you over communicate as much as possible.

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Like you go and tell like, what's working, what's not working, what you're

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going to deliver and deliver even before the deadlines is all of these things

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are like bits that you need to avoid.

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So generally, like let's say, you know, like jump into like one of these roles,

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there are a bunch of stuff online to read about. This is like a summary of it.

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So some of them have lead code that I think like most of the interviews does

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not have lead code.

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They look at like practical coding skills, like how do you build RAG system,
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They look at like practical coding skills, like how do you build RAG system,

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like LLM API, like how do you do the retry, how do you write the emails.

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All these like practical scenarios, like system design. So those are like the

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technical sides you get asked on.

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And then you get this decomposition case anthropic is quite popular for that.

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Generally like a case study question that you get like you get a relatively

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abstract problem and then you need to like break it down, ask the right

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question, and then sort of like copy the solution with the right kind of
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question, and then sort of like copy the solution with the right kind of

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process.

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And so another aspect is actually trying to like make sure that how do you make

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sure the system works properly. So these are things we discussed earlier about

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emails.

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And you're looking at setting up things in a way, even if things fails, you

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should know.

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So like, let's say if you build like a RAG system, like how do you know if it's

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working, like you have like a metrics into track.

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Things like whether the entire system works, whether the extraction work,
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Things like whether the entire system works, whether the extraction work,

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whether the generation works, the RAG system. So all of these needs to get

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checked.

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That's why it's important, like you define like what success looks like and

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define the metrics needed to improve that.

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So all of these needs to be like used earlier, but when you start building a

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few more, you see the patterns and then you can reuse the processes and the

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checks and balances going going through.

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And these are the questions you get asked as well.

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So for my skills, my point of view, so most of the technical skills are covered
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So for my skills, my point of view, so most of the technical skills are covered

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in the program that you're doing, and J bootcamp. So the foundation will be

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shipping real endpoints, RAG systems, vector databases, HMS orchestration,

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multi-systems, sub-agent systems,

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sort of AI emails, and understanding agentic memory and deploying things,

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actually building things is the only way to learn this.

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There's no point like studying theory around this, even like notebooks wouldn't

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cut it.

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So you need like more away from notebooks, how do you like deploy something,
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So you need like more away from notebooks, how do you like deploy something,

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understand Docker, how do you like deploy something to Brenda, GCP, AWS, all of

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these things are needed so that you understand getting something out in the

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world.

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So not just AI concepts, but also like the deployments and how do you build

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something and deploy it.

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Apart from that, there's like some other soft skills that are needed.

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Like these are even though I say soft skills are like properly hard skill to

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learn.

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You need to like understand how you're looking at sort of like addressing

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ambiguous problems.

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How do you decompose that to actually like understand the requirements and
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How do you decompose that to actually like understand the requirements and

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confidence solution doing onsite discovery, how do you do that.

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So sometimes this could be remote as well.

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I've seen like mix of both, given remote work is quite prominent right now, and

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I like, how do you like make sure you went with the team and then like sort of

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push something.

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And in enterprise, we all know it is not straightforward, not like a startup,

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you can like build things much faster, you have to deal with the processes and

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then try and like push stuff out.

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And yeah, so I think like when it comes to roll out patterns, like so like
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And yeah, so I think like when it comes to roll out patterns, like so like

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human in the low building like small automations and then trying to expand

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beyond that.

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And then you actually like working with the client to actually do the handover

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and then train somebody in their team to actually take control of the agent or

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the process that you build is the best way that you can like actually handle

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things rather than

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giving just documentation and doing one demo, like you sort of like work with

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the team actually get it over the line so that you can mean they can maintain
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the team actually get it over the line so that you can mean they can maintain

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them long term, and you can sort of go and build another system with them.

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So that's the general outside process.

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But there's a bit of debate around like if FDs are like good for the company,

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because there are some rumors around like anthropic trying to steal IP,

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especially like buying, but sort of like farmer companies, etc.

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This is a different debate, which I haven't like we really thought about like

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with side to take, but I understand the the risk that this kind of like if you
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with side to take, but I understand the the risk that this kind of like if you

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have because you also bring in someone and like obviously there are like IP

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sharing that's happening for some reason if anthropic decided to build a

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competitive product.

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Yeah, there's a legal situation happening so that's like one side that we need

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to think about, but I think from from a job or a role point of view, this is

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like a really good role that's a lot of people are like sort of hiring for.

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Yeah. So, so some sort of caveats apart from this, this might involve traveling
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Yeah. So, so some sort of caveats apart from this, this might involve traveling

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. You're looking at 25 to 50% depending on the firm to travel, generally,

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because quite new career ladder is still a bit flatter still, but I'm sure like

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with time to also like go about and burnout is quite common.

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I think it comes from this like traveling and then like sort of like, yeah,

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probably is coming from working with external clients, but it is like, say like
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probably is coming from working with external clients, but it is like, say like

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, it's not all the time.

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And then you won't necessarily get straight away to create models and like this

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fancy stuff. You'll have to like work with the data plumbing and all the

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security aspects. It takes a bit of time to like get to building something

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quite cool.

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So you have to be comfortable with that.

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Yeah, so that's about the obviously check out the program that you're running.

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We're starting this week. It does have all the technical aspects needed for a

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role. And also we have like coffee cells to discuss like if you're going
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role. And also we have like coffee cells to discuss like if you're going

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00:26:30,670 --> 00:26:34,500
through an interview, like more than happy to guide you there on some of these

457
00:26:34,500 --> 00:26:34,500
roles.

458
00:26:34,500 --> 00:26:38,820
So I'll post. Yeah, I saw some questions, but if you guys want to ask questions

459
00:26:38,820 --> 00:26:42,740
on me to yourself and ask, I'll pick the questions in the chat as well, while I

460
00:26:42,740 --> 00:26:43,500
'm doing the

461
00:26:48,500 --> 00:26:48,990
So a long ago says, if we need core engineering skill deployment problem

462
00:26:48,990 --> 00:26:55,290
solving skills, yeah, pretty much. Yes. And also like, I would add a bit more

463
00:26:55,290 --> 00:27:00,690
like, I'll say client facing like skills like you need to like deal with
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464
00:26:55,290 --> 00:27:00,690
like, I'll say client facing like skills like you need to like deal with

465
00:27:00,690 --> 00:27:05,500
clients and then like make sure these things get deployed.

466
00:27:05,500 --> 00:27:12,650
So I've seen like, so for example, Google does still check late code like

467
00:27:12,650 --> 00:27:18,310
Google FD role. So they have read code, things around like traditional medium

468
00:27:18,310 --> 00:27:19,500
level lead code.

469
00:27:19,500 --> 00:27:26,970
That is there. But at the same time, you're looking at like, especially API

470
00:27:26,970 --> 00:27:30,500
design, Docker, et cetera.
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471
00:27:26,970 --> 00:27:30,500
design, Docker, et cetera.

472
00:27:30,500 --> 00:27:35,500
Frank has very curious how we did the slides best looking I have seen.

473
00:27:35,500 --> 00:27:41,750
This is Claude by the way, just from design. So I'm running actually, yeah,

474
00:27:41,750 --> 00:27:43,500
design by Claude and building through cursor.

475
00:27:43,500 --> 00:27:49,500
So if I flip to this side, this is the select that I'm creating.

476
00:27:49,500 --> 00:27:53,500
I saw like an error here in the slide. I've just prompted it.

477
00:27:53,500 --> 00:27:59,500
Yeah. So, yeah. So.

478
00:27:59,500 --> 00:28:04,500
Hey, this is one question. Yeah.
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479
00:27:59,500 --> 00:28:04,500
Hey, this is one question. Yeah.

480
00:28:04,500 --> 00:28:09,540
So in your experience for a three question, right. So are you dealing with

481
00:28:09,540 --> 00:28:16,110
mostly like local models or you mostly dealing with cloud or chat GPT because

482
00:28:16,110 --> 00:28:19,950
why this question is most companies are reluctant to share their information

483
00:28:19,950 --> 00:28:20,500
with the public cloud.

484
00:28:20,500 --> 00:28:24,710
And they won't have like an on premises or in house a modern to the train. So

485
00:28:24,710 --> 00:28:26,500
what is it if you want.

486
00:28:26,500 --> 00:28:31,690
Yeah, I think so it depends. Right. So, for example, if the role, if the roles
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487
00:28:26,500 --> 00:28:31,690
Yeah, I think so it depends. Right. So, for example, if the role, if the roles

488
00:28:31,690 --> 00:28:35,500
are pretty much hired by like lead, like sort of product companies, right.

489
00:28:35,500 --> 00:28:40,200
Motor providers or it could be, let's say, anthropic Google, they have

490
00:28:40,200 --> 00:28:42,500
something to sell rather than.

491
00:28:42,500 --> 00:28:46,500
So they might have open source model. They'll help you with that.

492
00:28:46,500 --> 00:28:50,500
But the idea is that you act as the extension to go and help them rather than.

493
00:28:50,500 --> 00:28:54,170
So if I'm a farmer company, I wouldn't get a forty per engineer. That doesn't

494
00:28:54,170 --> 00:28:57,570
make sense in that case. I would get an engineer like to implement that like

495
00:28:57,570 --> 00:28:58,500
open source model.

496
00:28:58,500 --> 00:29:02,780
So, so the definition is that you have an engineer who goes into the client's
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497
00:28:58,500 --> 00:29:02,780
So, so the definition is that you have an engineer who goes into the client's

498
00:29:02,780 --> 00:29:04,500
site and build something.

499
00:29:04,500 --> 00:29:08,820
So whether it's the open source model, like for example, if it's open source

500
00:29:08,820 --> 00:29:11,500
project, yes, for sure. Like, for example, it's a misagents.

501
00:29:11,500 --> 00:29:15,780
So it's open source and you get like somebody like who can deploy that open

502
00:29:15,780 --> 00:29:16,500
source.

503
00:29:16,500 --> 00:29:19,070
And while doing that, yeah, for sure, you think about the different models, et

504
00:29:19,070 --> 00:29:23,410
cetera. But the idea is simple. Like you, you, you be this extension of a

505
00:29:23,410 --> 00:29:26,500
product company to go and actually implement their tools within that.

506
00:29:26,500 --> 00:29:28,500
So does that answer the question?

507
00:29:28,500 --> 00:29:32,500
Yes. Yes. Pretty much. Yes. Thank you very much.
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508
00:29:28,500 --> 00:29:32,500
Yes. Yes. Pretty much. Yes. Thank you very much.

509
00:29:32,500 --> 00:29:33,500
Cool.

510
00:29:33,500 --> 00:29:35,500
All right.

511
00:29:35,500 --> 00:29:40,790
Sanjalia. So first person should become an engineer and then FTE. Yeah. I mean,

512
00:29:40,790 --> 00:29:42,500
yeah, for sure. I think it makes a lot of sense.

513
00:29:42,500 --> 00:29:46,740
You need to like properly understand the engineering to be a FTE. So we

514
00:29:46,740 --> 00:29:48,500
technical as much as possible.

515
00:29:48,500 --> 00:29:53,280
And then working with client projects, like actually like pick a client project

516
00:29:53,280 --> 00:29:58,070
that gives you the ability to try and build something and then like deal with

517
00:29:58,070 --> 00:30:02,500
clients like that's where it's very hard to like recreate that into experience
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518
00:29:58,070 --> 00:30:02,500
clients like that's where it's very hard to like recreate that into experience

519
00:30:02,500 --> 00:30:02,500
rather than doing that.

520
00:30:02,500 --> 00:30:06,740
Then you'll see like, okay, what does actually this coping step means? Like,

521
00:30:06,740 --> 00:30:09,500
what does this data engineering plumbing means? It's not just a word.

522
00:30:09,500 --> 00:30:12,610
It's actually like you have to like work with the teams like to get these

523
00:30:12,610 --> 00:30:13,500
things moving.

524
00:30:13,500 --> 00:30:17,760
And so that has like different teams and different communication styles. You

525
00:30:17,760 --> 00:30:23,270
need to like talk the lingo that different teams users and like when they are

526
00:30:23,270 --> 00:30:28,500
sort of like, so you'll see this like, like, socially IT departments.

527
00:30:28,500 --> 00:30:33,690
Like sometimes if you find an easy going person, it's pretty hard to work, but
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528
00:30:28,500 --> 00:30:33,690
Like sometimes if you find an easy going person, it's pretty hard to work, but

529
00:30:33,690 --> 00:30:37,850
there are like people who are a bit hard to work with, could be there like over

530
00:30:37,850 --> 00:30:38,500
worked.

531
00:30:38,500 --> 00:30:42,500
So it's very hard to get on top of their priority list to get something done.

532
00:30:42,500 --> 00:30:47,500
So you have to like actually like, yeah, be nice to people like work with them.

533
00:30:47,500 --> 00:30:51,300
And you don't have necessarily a lot of time to build a relationship because

534
00:30:51,300 --> 00:30:54,760
already they have had like a lot of time with the existing team and the

535
00:30:54,760 --> 00:30:57,500
existing demand coming from the team would have more prior to than you.

536
00:30:57,500 --> 00:31:01,240
So how do you do that? So you have to like be extra nice, like be really like
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537
00:30:57,500 --> 00:31:01,240
So how do you do that? So you have to like be extra nice, like be really like

538
00:31:01,240 --> 00:31:04,500
over communicate and sort of like play a bit of politics.

539
00:31:04,500 --> 00:31:07,250
All of these things comes into play like me and I have to not just the

540
00:31:07,250 --> 00:31:08,500
technicals other things.

541
00:31:08,500 --> 00:31:12,030
So it's like you wear multiple hats. So that's why I say like you have to be

542
00:31:12,030 --> 00:31:15,770
technical to defend yourself, but also you need to have a bit more senior

543
00:31:15,770 --> 00:31:17,500
experience like how to like deal with this.

544
00:31:17,500 --> 00:31:21,400
If you come from Google, open AI for sure, you have some leverage people like

545
00:31:21,400 --> 00:31:25,500
to listen to you, but let's say if you're like not Google, open AI and traffic,

546
00:31:25,500 --> 00:31:29,540
you probably need to like work a bit hard to like, like have some authority

547
00:31:29,540 --> 00:31:30,500
within those teams.
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548
00:31:29,540 --> 00:31:30,500
within those teams.

549
00:31:30,500 --> 00:31:35,220
So that's my sort of like take on this. So it depends on the firm that you work

550
00:31:35,220 --> 00:31:35,500
with.

551
00:31:40,500 --> 00:31:45,680
It's a question if I don't want to move into a specific role, but I want to use

552
00:31:45,680 --> 00:31:48,500
that they.

553
00:31:48,500 --> 00:31:55,500
Yeah, in my day of the role, I mean, roles are not tech supporter.

554
00:31:55,500 --> 00:32:00,500
What's the question, Alish? So.
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555
00:31:55,500 --> 00:32:00,500
What's the question, Alish? So.

556
00:32:00,500 --> 00:32:04,500
Hi, this is Ali. I'm the one asking the question. Yeah, go for it.

557
00:32:04,500 --> 00:32:09,230
So just to be more specific, like, you know, like, like, I'm, you know, like in

558
00:32:09,230 --> 00:32:13,930
my previous role, I did kind of use AI, like, you know, like, you know, my

559
00:32:13,930 --> 00:32:18,670
previous role, I was a desktop engineer and I kind of was using AI, you know,

560
00:32:18,670 --> 00:32:20,500
to for like making, like,

561
00:32:20,500 --> 00:32:34,530
PowerShell scripts or, or the ones I had making sure they work good. I guess
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562
00:32:20,500 --> 00:32:34,530
PowerShell scripts or, or the ones I had making sure they work good. I guess

563
00:32:34,530 --> 00:32:34,780
what I'm, I mean, I guess what I'm trying to ask is if you say you don't

564
00:32:34,780 --> 00:32:35,130
necessarily want to move into an AI specific role, like the kinds of things you

565
00:32:35,130 --> 00:32:35,500
were talking about, but like,

566
00:32:35,500 --> 00:32:40,260
but I, but, but as I've been applying for jobs, you know, I'm from infrastruct

567
00:32:40,260 --> 00:32:44,950
ural to like, you know, desktop engineering roles, almost every job posting

568
00:32:44,950 --> 00:32:50,750
wants somebody who's, you know, not just AI literate, but like they want proof

569
00:32:50,750 --> 00:32:55,270
that, okay, you've used AI, whether it's from like, you know, creating a script

570
00:32:55,270 --> 00:32:56,500
or, or, or using it.

571
00:32:56,500 --> 00:33:01,700
So I'm, I mean, I guess I'm just asking, like, you know, as I'm doing other
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572
00:32:56,500 --> 00:33:01,700
So I'm, I mean, I guess I'm just asking, like, you know, as I'm doing other

573
00:33:01,700 --> 00:33:07,080
certifications in my, that my job roles require like, how much should I go into

574
00:33:07,080 --> 00:33:08,500
the weeds in AI?

575
00:33:08,500 --> 00:33:13,000
I guess, you know, like, I guess, where should I find a balance? If that may, I

576
00:33:13,000 --> 00:33:15,500
don't know if that would question if I'm asking it the right way.

577
00:33:15,500 --> 00:33:19,780
You know, maybe I understand, like, so you try and, like, figure out, like how

578
00:33:19,780 --> 00:33:24,500
to upscale yourself in a way that it fits with this AI sort of demand, I guess.

579
00:33:24,500 --> 00:33:29,120
So, so, sweet much depends on, like, where in the target, like, but having this

580
00:33:29,120 --> 00:33:33,610
AI foundation there, it's going to help be helpful for any, like, sort of, like
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581
00:33:29,120 --> 00:33:33,610
AI foundation there, it's going to help be helpful for any, like, sort of, like

582
00:33:33,610 --> 00:33:35,780
, role that you're going to go for, whether it's like infrastructure play,

583
00:33:35,780 --> 00:33:40,500
whether it's like a F.D. general engineer, like, even like traditional software

584
00:33:40,500 --> 00:33:42,500
engineer, like, depending on what, what that role is.

585
00:33:42,500 --> 00:33:45,900
So, yeah, definitely, like, I would say, like, spend a bit of time

586
00:33:45,900 --> 00:33:49,830
understanding things like RAG, multi-agent systems, sub-agent, all of these

587
00:33:49,830 --> 00:33:50,500
stuff.

588
00:33:50,500 --> 00:33:54,500
This is not hard, like, but you need to, like, spend a bit of time building it.

589
00:33:54,500 --> 00:33:57,180
You just need, like, two to three months, focus time, and then you're really

590
00:33:57,180 --> 00:33:57,500
good.

591
00:33:57,500 --> 00:34:01,500
And then on top of that, you can slowly, like, do incremental learning on that.
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592
00:33:57,500 --> 00:34:01,500
And then on top of that, you can slowly, like, do incremental learning on that.

593
00:34:01,500 --> 00:34:05,800
And after that, like, you pick some sort of infrastructure that you're going to

594
00:34:05,800 --> 00:34:10,500
focus on, like, for example, like, a lot of demand for, like, machine learning.

595
00:34:10,500 --> 00:34:14,850
Like, training engineers, like, who can set up the infrastructure with AWS, GCP

596
00:34:14,850 --> 00:34:17,500
, Azure, whatever that is, you can make a play.

597
00:34:17,500 --> 00:34:22,550
And there's, like, there's lack of certified GCP engineers. I am trying to hire

598
00:34:22,550 --> 00:34:24,500
some people. It's very hard for me to find AWS.

599
00:34:24,500 --> 00:34:29,280
I think, like, there's still demand, and because the district, like, the market

600
00:34:29,280 --> 00:34:33,500
, still, it's massive for AWS, so it's also a safe bet.
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601
00:34:29,280 --> 00:34:33,500
, still, it's massive for AWS, so it's also a safe bet.

602
00:34:33,500 --> 00:34:37,270
And I'm also seeing a casual, right, Microsoft Stack. So you can pick one of

603
00:34:37,270 --> 00:34:41,550
these and then, like, certify yourself, like, in whatever that interests you,

604
00:34:41,550 --> 00:34:42,500
and then, like, go to the interview.

605
00:34:42,500 --> 00:34:45,440
So that's going to give you the infrastructure side of things, and then you

606
00:34:45,440 --> 00:34:46,500
have the AI knowledge.

607
00:34:46,500 --> 00:34:49,820
So I would take that approach to, like, try and, like, because practical skills

608
00:34:49,820 --> 00:34:54,580
value a lot, and having these certifications and the knowledge will help you,

609
00:34:54,580 --> 00:34:56,500
like, land your next role.

610
00:34:56,500 --> 00:35:01,340
All right, thank you. Okay, all right. I mean, reach out if there are other
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611
00:34:56,500 --> 00:35:01,340
All right, thank you. Okay, all right. I mean, reach out if there are other

612
00:35:01,340 --> 00:35:02,500
questions, yeah.

613
00:35:02,500 --> 00:35:10,340
So follow this path to obstacle and get prepared for the role. Seems a bit

614
00:35:10,340 --> 00:35:11,500
blurred, did.

615
00:35:11,500 --> 00:35:15,410
Yeah, so what you're looking at is, like, for example, depends on the role that

616
00:35:15,410 --> 00:35:16,500
you're going for.

617
00:35:16,500 --> 00:35:19,900
If you're going for, like, general AI roles, AI player, engineer, engineer,

618
00:35:19,900 --> 00:35:24,500
engineer, you need to, like, be able to, like, build and deploy things.

619
00:35:24,500 --> 00:35:28,540
Because that's, that's the grandmother. Can you actually think about building,

620
00:35:28,540 --> 00:35:31,500
deploying something to production? Can you get, sort of, maintain that?
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621
00:35:28,540 --> 00:35:31,500
deploying something to production? Can you get, sort of, maintain that?

622
00:35:31,500 --> 00:35:35,700
So all of these aspects like CSC, Docker, all of these things are needed, but

623
00:35:35,700 --> 00:35:39,830
also important to understand how AI models work, and all of these things going

624
00:35:39,830 --> 00:35:40,500
to help you.

625
00:35:40,500 --> 00:35:45,820
So, for example, like, Google interviews, right, so they do check most of the

626
00:35:45,820 --> 00:35:46,500
time.

627
00:35:46,500 --> 00:35:55,500
Like, give you, like, a, this open-ended question. You need to, like, figure

628
00:35:55,500 --> 00:35:55,500
out, like, what are the requirements, what are the, so, edge cases, and then,

629
00:35:55,500 --> 00:35:55,500
like, copy the system design.

630
00:35:55,500 --> 00:35:58,060
And after that, you need to, like, figure out, like, how are you going to

631
00:35:58,060 --> 00:36:00,950
evaluate the success, things like, yeah, emails, like, how do you put those
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632
00:35:58,060 --> 00:36:00,950
evaluate the success, things like, yeah, emails, like, how do you put those

633
00:36:00,950 --> 00:36:01,500
things in?

634
00:36:01,500 --> 00:36:06,040
And, like, so, this is a whole, like, process that anything. So only way that

635
00:36:06,040 --> 00:36:08,990
you can actually answer those questions, rather than memorizing everything

636
00:36:08,990 --> 00:36:12,500
possible, it's just actually build stuff and, like, putting things out there.

637
00:36:12,500 --> 00:36:16,160
And, like, I have this concept called, like, building public. I get everybody

638
00:36:16,160 --> 00:36:19,500
to, like, if my courses like to build things in public and put it out there,

639
00:36:19,500 --> 00:36:21,500
you get, like, real feedback from people.

640
00:36:21,500 --> 00:36:24,920
It could be sometimes painful, but that's a faster way to learn, rather than

641
00:36:24,920 --> 00:36:28,620
you building stuff and just, like, studying your own, like, because that takes

642
00:36:28,620 --> 00:36:29,500
a lot of time.

643
00:36:29,500 --> 00:36:33,400
So it's a, at least, like, build stuff and put it out there. If you can, like,
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644
00:36:29,500 --> 00:36:33,400
So it's a, at least, like, build stuff and put it out there. If you can, like,

645
00:36:33,400 --> 00:36:36,500
build a quick app, open-source project, put it out there.

646
00:36:36,500 --> 00:36:40,770
That's going to carry a lot more value than you trying to, like, memorize 150

647
00:36:40,770 --> 00:36:44,500
really good questions, still sometimes needed, but that's rather than that I

648
00:36:44,500 --> 00:36:45,500
would, like, build stuff.

649
00:36:45,500 --> 00:36:49,360
And I would hire somebody, like, who can build stuff much faster, rather than

650
00:36:49,360 --> 00:36:53,960
who's, like, really liking to, like, understanding, I don't know, like, just

651
00:36:53,960 --> 00:36:56,500
patterns and how to solve good problems.

652
00:36:56,500 --> 00:37:00,500
That's my sort of view on it.
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653
00:36:56,500 --> 00:37:00,500
That's my sort of view on it.

654
00:37:00,500 --> 00:37:04,900
There's another question, what about focusing on a provider, or not, like, GCP,

655
00:37:04,900 --> 00:37:07,500
AWS, etc., should we focus on one of them?

656
00:37:07,500 --> 00:37:11,390
Yeah, I would say, like, picking one of them is better, rather than trying to,

657
00:37:11,390 --> 00:37:15,930
like, learn everything at least first, like, because you have, like, 80%, like,

658
00:37:15,930 --> 00:37:18,500
shared, sort of, like, process between them.

659
00:37:18,500 --> 00:37:22,830
So if you, like, master one of them is very easy to, like, get across, and then

660
00:37:22,830 --> 00:37:27,460
, yeah, I would say pick one and master that as much as possible, rather than,

661
00:37:27,460 --> 00:37:29,500
like, picking random things.

662
00:37:29,500 --> 00:37:32,500
Yeah, I think that's my sort of take on it.
WEBVTT

663
00:37:29,500 --> 00:37:32,500
Yeah, I think that's my sort of take on it.

664
00:37:32,500 --> 00:37:36,520
It could be wrong, like, that's my view on it, like, I mean, on that I could be

665
00:37:36,520 --> 00:37:37,500
wrong as well.

666
00:37:37,500 --> 00:37:42,500
But, yeah, I think I'd be best to, like, master something, and have this.

667
00:37:42,500 --> 00:37:46,230
So things like, cloud certifications are good, because they do cover, like,

668
00:37:46,230 --> 00:37:50,500
some of the latest architecture patterns, like, multi-agent, sub-agent systems.

669
00:37:50,500 --> 00:37:55,500
It's also interesting, and AWS has a machine learning certification.

670
00:37:55,500 --> 00:37:56,500
It's also interesting.

671
00:37:56,500 --> 00:37:59,500
I think they have now, like, an engineering one as well.

672
00:37:59,500 --> 00:38:02,500
This could be interesting, but it takes a lot of time.
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673
00:37:59,500 --> 00:38:02,500
This could be interesting, but it takes a lot of time.

674
00:38:02,500 --> 00:38:06,500
So you can start building just before you jump into it.

675
00:38:06,500 --> 00:38:10,500
All right, I'm just checking if there are more questions.

676
00:38:21,500 --> 00:38:23,730
Do you have recent JD for these with technical skills, BRS skills, and any

677
00:38:23,730 --> 00:38:30,500
other that does not fit in about two?
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678
00:38:23,730 --> 00:38:30,500
other that does not fit in about two?

679
00:38:30,500 --> 00:38:35,500
Is it, like, my previous slide, talking about Miho?

680
00:38:35,500 --> 00:38:43,500
Yeah, go for it.

681
00:38:43,500 --> 00:38:50,500
Yeah, hi, I think your previous slide talked about it, you know, normally what,

682
00:38:50,500 --> 00:38:55,950
but what puts it in the, you know, the LinkedIn or something, no, which gives

683
00:38:55,950 --> 00:38:56,500
you.

684
00:38:56,500 --> 00:38:59,500
Okay, this is the background of the company.

685
00:38:59,500 --> 00:39:01,500
This is the work which we are doing.
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686
00:38:59,500 --> 00:39:01,500
This is the work which we are doing.

687
00:39:01,500 --> 00:39:03,500
These are the skills expected.

688
00:39:03,500 --> 00:39:06,500
These are the behavioral skills expected.

689
00:39:06,500 --> 00:39:11,720
These are the additional knowledge or something which is expected and all those

690
00:39:11,720 --> 00:39:12,500
things.

691
00:39:12,500 --> 00:39:13,500
Some categories there.

692
00:39:13,500 --> 00:39:15,500
So I wanted to see this.

693
00:39:15,500 --> 00:39:17,500
This looks after 30 minutes.

694
00:39:17,500 --> 00:39:23,080
Initially, I was very excited, but later on, it feels like it's way, way

695
00:39:23,080 --> 00:39:27,500
tougher than what it is looking like.

696
00:39:27,500 --> 00:39:30,500
It is like, I'm not going to like sugarcoat it, right?
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697
00:39:27,500 --> 00:39:30,500
It is like, I'm not going to like sugarcoat it, right?

698
00:39:30,500 --> 00:39:34,480
That's like, I mean, I can call it was in 30 minutes, but I'm going to say like

699
00:39:34,480 --> 00:39:36,500
somebody to become an like a 44 engineer.

700
00:39:36,500 --> 00:39:43,180
You do get like, I wouldn't say like, like quite tough, but you need to like be

701
00:39:43,180 --> 00:39:46,500
able to like, if you're technical already, like, yeah, you'll be fine.

702
00:39:46,500 --> 00:39:48,880
It's not like, like, for example, if I don't know your background, I would love

703
00:39:48,880 --> 00:39:49,500
to understand.

704
00:39:49,500 --> 00:39:53,500
I'm not a, I'm not a proper technical.

705
00:39:53,500 --> 00:39:57,890
I have very good hands on on the Oracle SQL and all those things, not recent

706
00:39:57,890 --> 00:40:02,500
times, but I'm working on a ZL delivery developer.
WEBVTT

707
00:39:57,890 --> 00:40:02,500
times, but I'm working on a ZL delivery developer.

708
00:40:02,500 --> 00:40:03,500
That's totally fine.

709
00:40:03,500 --> 00:40:07,130
Nothing like so because like with like a lot of these coding agents, like a lot

710
00:40:07,130 --> 00:40:10,500
of people now code, like, doesn't matter like their background is.

711
00:40:10,500 --> 00:40:13,940
And while you build stuff, you learn like, so that's the best to learn rather

712
00:40:13,940 --> 00:40:15,500
than like starting theories.

713
00:40:15,500 --> 00:40:18,180
It was a case like maybe two, three years ago, right now, like you've got cloud

714
00:40:18,180 --> 00:40:19,500
code, code X, whatever that is.

715
00:40:19,500 --> 00:40:23,500
And people with no technical background, like learn these things much faster.

716
00:40:23,500 --> 00:40:27,500
And you be in a, you be totally fine because you have technical knowledge.

717
00:40:27,500 --> 00:40:33,190
And so, yeah, to take two or three months, like actually like, get these things
WEBVTT

718
00:40:27,500 --> 00:40:33,190
And so, yeah, to take two or three months, like actually like, get these things

719
00:40:33,190 --> 00:40:34,500
down, like the knowledge.

720
00:40:34,500 --> 00:40:36,500
And then afterwards try and applying them.

721
00:40:36,500 --> 00:40:40,500
So I think like, so within your own firm, try to find these kind of like roles

722
00:40:40,500 --> 00:40:43,710
that you can like project that you can sort of like story lean into and then

723
00:40:43,710 --> 00:40:44,500
you get the real experience.

724
00:40:44,500 --> 00:40:46,500
So that's the way to handle this.

725
00:40:46,500 --> 00:40:50,500
So don't worry about like, don't get like faced off by looking at the syllabus.

726
00:40:50,500 --> 00:40:51,500
It's quite doable.

727
00:40:51,500 --> 00:40:55,500
So I would totally back you to like learn this stuff.

728
00:40:55,500 --> 00:40:56,500
So don't worry about it.

729
00:40:56,500 --> 00:41:00,500
Unfortunately, I'm in a situation where I'm actually looking for a job setting
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730
00:40:56,500 --> 00:41:00,500
Unfortunately, I'm in a situation where I'm actually looking for a job setting

731
00:41:00,500 --> 00:41:04,500
up a couple of more than a couple of months.

732
00:41:04,500 --> 00:41:05,500
Okay.

733
00:41:05,500 --> 00:41:12,500
And I've been, I'm just going to be very open for fair with you.

734
00:41:12,500 --> 00:41:17,500
I've been joining some more than other modern lightning lessons to understand.

735
00:41:17,500 --> 00:41:18,500
Where do I fit?

736
00:41:18,500 --> 00:41:24,500
Where do I, you know, how do I try to map my skill and I can, you know,

737
00:41:24,500 --> 00:41:25,500
transfer my skills?

738
00:41:25,500 --> 00:41:28,500
Or it, I can easily switch into those.

739
00:41:28,500 --> 00:41:30,500
This is one of those things.
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740
00:41:28,500 --> 00:41:30,500
This is one of those things.

741
00:41:30,500 --> 00:41:36,900
I'm not necessarily, I'm targeting FD, but I'm trying to switch making a major

742
00:41:36,900 --> 00:41:39,500
pivot in my career kind of thing.

743
00:41:39,500 --> 00:41:40,500
I hear, I hear.

744
00:41:40,500 --> 00:41:41,500
Yeah.

745
00:41:41,500 --> 00:41:46,010
So like to switch to FD, I think like the co-components are these, like try and

746
00:41:46,010 --> 00:41:49,500
understand like these aspects, like to be able to like build rack systems,

747
00:41:49,500 --> 00:41:52,500
build like agents, and then like things like memory.

748
00:41:52,500 --> 00:41:56,890
Like how do you like understand like loop engineering, et cetera, all of these

749
00:41:56,890 --> 00:42:00,500
things, which is you can learn within like one and a half months time, right?
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750
00:41:56,890 --> 00:42:00,500
things, which is you can learn within like one and a half months time, right?

751
00:42:00,500 --> 00:42:02,500
So that's the foundation that you need.

752
00:42:02,500 --> 00:42:06,200
So that when you have that foundation, you can go and apply to like, okay, if

753
00:42:06,200 --> 00:42:08,500
you have client experience, you go to FD.

754
00:42:08,500 --> 00:42:13,500
If you have product management experience, go to a product manager role.

755
00:42:13,500 --> 00:42:16,330
Or if you have like, for example, a bit more software engineering, like data

756
00:42:16,330 --> 00:42:18,500
background, you can go to like AI engineering role.

757
00:42:18,500 --> 00:42:22,920
So, so you use this knowledge, plus like whatever you already had to find this

758
00:42:22,920 --> 00:42:23,500
role.

759
00:42:23,500 --> 00:42:29,020
So you might need to like learn a bit, like collect a few more like experiences

760
00:42:29,020 --> 00:42:33,210
could be a subset, like a certificate that would help your role, but like
WEBVTT

761
00:42:29,020 --> 00:42:33,210
could be a subset, like a certificate that would help your role, but like

762
00:42:33,210 --> 00:42:36,500
having like having this grounded knowledge, like it's quite like.

763
00:42:36,500 --> 00:42:40,510
So this is like learning, I would say coding, making like five, six, like 10

764
00:42:40,510 --> 00:42:44,460
years ago, like understanding like, okay, I can I quote in Java Python like, so

765
00:42:44,460 --> 00:42:47,500
that's going to be a very versatile skill set to have.

766
00:42:47,500 --> 00:42:49,500
So that's my sort of take on it.

767
00:42:49,500 --> 00:42:50,500
Okay.

768
00:42:50,500 --> 00:42:51,500
Yeah.

769
00:42:51,500 --> 00:42:52,500
Okay.

770
00:42:52,500 --> 00:42:58,960
Oh, do you, do you, do you think that, okay, this is another question, if you

771
00:42:58,960 --> 00:43:01,500
don't mind to answer this one.
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772
00:42:58,960 --> 00:43:01,500
don't mind to answer this one.

773
00:43:01,500 --> 00:43:08,730
I'm totally getting confused with a number of people talking about use Claude

774
00:43:08,730 --> 00:43:15,500
and flavors of Claude, and somebody says use chat JPT or open AI products.

775
00:43:15,500 --> 00:43:19,500
And somebody says, no, go to Google's, Gemini and other things.

776
00:43:19,500 --> 00:43:24,780
Of course, Gemini have kept it aside, but out of these two being, see, as an

777
00:43:24,780 --> 00:43:29,420
individual and paying, you know, 20 dollar per month, and not able to learn

778
00:43:29,420 --> 00:43:30,500
something out of it.
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779
00:43:29,420 --> 00:43:30,500
something out of it.

780
00:43:30,500 --> 00:43:34,500
It's going to be very costly for even though it is 20 dollar only.

781
00:43:34,500 --> 00:43:39,500
But what is your view on Claude versus open AI.

782
00:43:39,500 --> 00:43:42,500
I mean, this is not to influence anything.

783
00:43:42,500 --> 00:43:44,500
What is your choice?

784
00:43:44,500 --> 00:43:45,500
Yeah.

785
00:43:45,500 --> 00:43:50,500
So, I mean, I used to like use a lot of open AI before.

786
00:43:50,500 --> 00:43:54,500
Right now, I have a mix with in Claude code and cursor.

787
00:43:54,500 --> 00:43:56,500
So cursor provides like all the models you need.

788
00:43:56,500 --> 00:44:02,970
Right? So, so that you get the open AI models, you get the Claude code models,
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789
00:43:56,500 --> 00:44:02,970
Right? So, so that you get the open AI models, you get the Claude code models,

790
00:44:02,970 --> 00:44:04,500
like all of the models, so you can like use something like that.

791
00:44:04,500 --> 00:44:07,500
So, yeah, so that's the general sense.

792
00:44:07,500 --> 00:44:11,140
And they compete with each other, right? Like, so, like, I mean, let's come

793
00:44:11,140 --> 00:44:12,500
back, Codex and Claude code.

794
00:44:12,500 --> 00:44:16,520
In two months time, Codex, I mean, Codex getting very popular these days, it

795
00:44:16,520 --> 00:44:17,500
might surpass.

796
00:44:17,500 --> 00:44:22,360
The difference between these two is very minimal, like, I wouldn't say like

797
00:44:22,360 --> 00:44:23,500
very minimal.

798
00:44:23,500 --> 00:44:26,500
And there's a lot of marketing hype behind this, so just disregard that.

799
00:44:26,500 --> 00:44:29,500
So, stick to one of these and then you'll be fine.

800
00:44:29,500 --> 00:44:32,240
Like, so there's Claude code with the Codex, stick to one of these and you'll
WEBVTT

801
00:44:29,500 --> 00:44:32,240
Like, so there's Claude code with the Codex, stick to one of these and you'll

802
00:44:32,240 --> 00:44:34,500
be, if you need, you can go to open code, that's not another option.

803
00:44:34,500 --> 00:44:39,740
But what the skill you need to learn is like not these models, like the

804
00:44:39,740 --> 00:44:41,500
providers, what you need to learn is like how to use AI.

805
00:44:41,500 --> 00:44:47,190
Like, how to use AI coding tools in general, to actually quote, like, that's it

806
00:44:47,190 --> 00:44:49,500
, like, to be not faced off by the technical stuff.

807
00:44:49,500 --> 00:44:54,760
Not picking between open AI versus anthropic, because they'll compete, outcomp

808
00:44:54,760 --> 00:44:56,500
ete each other all the time.

809
00:44:56,500 --> 00:45:00,500
And they have like good engineers in both the teams and they'll figure it out.
WEBVTT

810
00:44:56,500 --> 00:45:00,500
And they have like good engineers in both the teams and they'll figure it out.

811
00:45:00,500 --> 00:45:03,840
Like, if maybe they're maybe one month delay to get to the point, but get

812
00:45:03,840 --> 00:45:06,500
comfortable with one of these and like stick to that.

813
00:45:06,500 --> 00:45:10,460
I mean, because it's not the option, like all these three are quite good, and

814
00:45:10,460 --> 00:45:12,500
you'll be fine, whatever you pick.

815
00:45:12,500 --> 00:45:19,130
You don't have to pick between, like, no need to have like, like, if you pick

816
00:45:19,130 --> 00:45:20,500
one, you lose.

817
00:45:20,500 --> 00:45:23,500
It's not the need to have in this one.

818
00:45:23,500 --> 00:45:24,500
If that helps.

819
00:45:24,500 --> 00:45:25,500
Yeah.

820
00:45:25,500 --> 00:45:26,500
Yeah.

821
00:45:26,500 --> 00:45:27,500
Thank you.

822
00:45:27,500 --> 00:45:29,500
I'll let you listen and ask his question.

823
00:45:29,500 --> 00:45:30,500
Yeah.
WEBVTT

824
00:45:29,500 --> 00:45:30,500
Yeah.

825
00:45:30,500 --> 00:45:31,500
Yeah.

826
00:45:31,500 --> 00:45:36,500
Yeah, let's go for it. Yeah, very quick here. Thanks so much for doing this.

827
00:45:36,500 --> 00:45:40,010
And shortly I mentioned product management. I'm currently interviewing with a

828
00:45:40,010 --> 00:45:42,500
couple of companies, mainly with the CPOs, right?

829
00:45:42,500 --> 00:45:46,500
I have a little bit of a hands-on experience as a PM now being building.

830
00:45:46,500 --> 00:45:47,500
Yeah.

831
00:45:47,500 --> 00:45:50,930
It is one phenomena right now talking about companies specifically here in

832
00:45:50,930 --> 00:45:51,500
Europe.

833
00:45:51,500 --> 00:45:53,500
I would go to an interview and technical one.

834
00:45:53,500 --> 00:45:55,500
There is a discussion about an AI native PM.

835
00:45:55,500 --> 00:45:56,500
What can you build?

836
00:45:56,500 --> 00:45:58,500
I even have a portfolio where I can show.

837
00:45:58,500 --> 00:46:03,090
But there is one thing that it's a little bit hard to pull off because even the
WEBVTT

838
00:45:58,500 --> 00:46:03,090
But there is one thing that it's a little bit hard to pull off because even the

839
00:46:03,090 --> 00:46:08,690
CPOs, like traditional CPOs, they kind of want something, but they exactly don

840
00:46:08,690 --> 00:46:10,500
't know what an in whom, right?

841
00:46:10,500 --> 00:46:14,240
If we want an AI native product manager that sort of decent that problem, right

842
00:46:14,240 --> 00:46:14,500
?

843
00:46:14,500 --> 00:46:17,090
And then when you go to an interview, the questions are so scattered and you're

844
00:46:17,090 --> 00:46:17,500
not linked.

845
00:46:17,500 --> 00:46:20,500
And it's very difficult sometimes to show the value you bring to the table.

846
00:46:20,500 --> 00:46:24,130
And when you get confused between traditional PM, should you now lean more

847
00:46:24,130 --> 00:46:27,860
towards AI and then you realize there is some knowledge gap, for example, on

848
00:46:27,860 --> 00:46:30,500
their end, that I'm afraid that they can misunderstand.
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849
00:46:27,860 --> 00:46:30,500
their end, that I'm afraid that they can misunderstand.

850
00:46:30,500 --> 00:46:31,500
It has happened one time.

851
00:46:31,500 --> 00:46:32,500
Okay.

852
00:46:32,500 --> 00:46:34,940
Was you, how would you approach in this situation where the knowledge is

853
00:46:34,940 --> 00:46:35,500
scattered?

854
00:46:35,500 --> 00:46:38,500
They don't know exactly what they want. How would you position yourself?

855
00:46:38,500 --> 00:46:41,500
Yeah, so I think like, so depends on the company.

856
00:46:41,500 --> 00:46:44,940
What I do is like, if I'm going to like a specific company and I'm going out

857
00:46:44,940 --> 00:46:48,500
heavily study the company, like what's the product they're building?

858
00:46:48,500 --> 00:46:52,560
And I also like understand like what sort of value I can bring to the table on

859
00:46:52,560 --> 00:46:53,500
that regard.

860
00:46:53,500 --> 00:46:56,280
So whatever the demos I'm building, what are the skills that I'm putting on my

861
00:46:56,280 --> 00:46:56,500
CV?

862
00:46:56,500 --> 00:46:59,990
What I'm going to talk about the narrative that I'm going to have is focused

863
00:46:59,990 --> 00:47:04,810
towards like helping them sort of like understand that what, like how your
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864
00:46:59,990 --> 00:47:04,810
towards like helping them sort of like understand that what, like how your

865
00:47:04,810 --> 00:47:08,500
skills are much more closer to what they're building.

866
00:47:08,500 --> 00:47:11,740
So in that way, it's a very relatively easy, like familiar for them to like

867
00:47:11,740 --> 00:47:15,350
figure out like, for example, if you're building like some sort of coding agent

868
00:47:15,350 --> 00:47:17,280
, like you can talk about like how you sort of do extensions to your coding

869
00:47:17,280 --> 00:47:17,500
agent.

870
00:47:17,500 --> 00:47:20,880
And like how about sort of skills you build all of these things. So all of

871
00:47:20,880 --> 00:47:23,840
these lingo is helpful for them. Like, so they might be talking about these

872
00:47:23,840 --> 00:47:25,500
things all the time within their sort of organization.

873
00:47:25,500 --> 00:47:30,180
So that's going to help them. So like talking in that language is going to be
WEBVTT

874
00:47:25,500 --> 00:47:30,180
So that's going to help them. So like talking in that language is going to be

875
00:47:30,180 --> 00:47:32,500
helpful beyond like you.

876
00:47:32,500 --> 00:47:38,020
So, so the other angle is like, I would recommend it's like, you be that person

877
00:47:38,020 --> 00:47:42,500
that AI native PM, like whatever that is, I people use different terms.

878
00:47:42,500 --> 00:47:47,130
The idea is that you have the PM hat, like the core of it, but using leveraging

879
00:47:47,130 --> 00:47:51,500
AI to actually like help with like rest of the work that you're doing.

880
00:47:51,500 --> 00:47:56,260
Whether it's like, like researching about your clients, like building POCs,

881
00:47:56,260 --> 00:48:00,760
like, and like doing automations, all of these things going to help you like
WEBVTT

882
00:47:56,260 --> 00:48:00,760
like, and like doing automations, all of these things going to help you like

883
00:48:00,760 --> 00:48:02,500
sort of like be that person.

884
00:48:02,500 --> 00:48:07,160
So you slowly get closer to being an engineer. So in that way, like you, you

885
00:48:07,160 --> 00:48:09,500
are like quite good. So they can't deny that.

886
00:48:09,500 --> 00:48:12,440
Right. So you have the PM knowledge, you can actually build stuff and you

887
00:48:12,440 --> 00:48:15,500
understand like what sort of things work and what sort of things to work.

888
00:48:15,500 --> 00:48:18,500
Sort of like, that's my take on it. Those are two things I would do.

889
00:48:18,500 --> 00:48:23,350
But looking at your specific scenario, the interview, I can give a bit more

890
00:48:23,350 --> 00:48:24,500
advice there.

891
00:48:24,500 --> 00:48:27,990
Absolutely. I can tell you very shortly. So my next one is basically with the

892
00:48:27,990 --> 00:48:29,500
CPU and a founder.

893
00:48:29,500 --> 00:48:33,360
They told me they have a task. I'm like, sure what the task is, is like, yeah,
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894
00:48:29,500 --> 00:48:33,360
They told me they have a task. I'm like, sure what the task is, is like, yeah,

895
00:48:33,360 --> 00:48:37,840
we don't know yet, but it would be like, as follows, we give you an account on

896
00:48:37,840 --> 00:48:38,500
cloud code.

897
00:48:38,500 --> 00:48:42,500
And we tell you something and you build for us live for one hour.

898
00:48:42,500 --> 00:48:47,760
Okay. Yep. And that's it. So, yeah. So something like that, it's very hard to

899
00:48:47,760 --> 00:48:51,490
like fake because you should be building every day to actually, because you

900
00:48:51,490 --> 00:48:55,500
then know like what sort of prompts to type, what sort of skills to use.

901
00:48:55,500 --> 00:48:58,470
All of these, how do you use plan mode? Like how do you switch between

902
00:48:58,470 --> 00:49:02,230
different models? All of these nuances. How about like, you know, one hour
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903
00:48:58,470 --> 00:49:02,230
different models? All of these nuances. How about like, you know, one hour

904
00:49:02,230 --> 00:49:02,500
period.

905
00:49:02,500 --> 00:49:05,900
Like, so like, like, if something fails, like, how do you like revert like,

906
00:49:05,900 --> 00:49:09,300
like what, like, you put like the logs in, you connect some sort of like, sorry

907
00:49:09,300 --> 00:49:10,500
, you test it, right?

908
00:49:10,500 --> 00:49:14,150
So for me, like, I have like one example, like, so you can like have a cloud

909
00:49:14,150 --> 00:49:18,500
extension, like to go through, like, to click through and get things working.

910
00:49:18,500 --> 00:49:22,240
I've connected MCP connection to my browser so that my cloud can actually talk

911
00:49:22,240 --> 00:49:26,190
to it, use property, all of these things going to be helpful, like beyond just

912
00:49:26,190 --> 00:49:28,500
like, just prompting to get something.

913
00:49:28,500 --> 00:49:32,830
So that's, that's, that's what they're looking for, only way to get over that
WEBVTT

914
00:49:28,500 --> 00:49:32,830
So that's, that's, that's what they're looking for, only way to get over that

915
00:49:32,830 --> 00:49:37,270
face is like to build every day, sadly, like, it's just now like, studying that

916
00:49:37,270 --> 00:49:40,500
's going to get you there, like, just be a person who builds every time.

917
00:49:40,500 --> 00:49:43,720
So like, obviously you mentioned you have portfolio, just extend them, like,

918
00:49:43,720 --> 00:49:46,970
like, look at like how we can sort of like, maybe you're like, just one product

919
00:49:46,970 --> 00:49:49,500
that you focus on and just like continuously improve that.

920
00:49:49,500 --> 00:49:53,380
That's going to give you like, like, yeah, like, deploy a message and like, see

921
00:49:53,380 --> 00:49:55,500
what, what that works and how it works.

922
00:49:55,500 --> 00:49:57,500
So all of these things are quite nuanced.

923
00:49:57,500 --> 00:50:00,510
And the more you build, and this is the best time to build, right, where you
WEBVTT

924
00:49:57,500 --> 00:50:00,510
And the more you build, and this is the best time to build, right, where you

925
00:50:00,510 --> 00:50:03,500
have like all these tools, like build out, say, leverage that.

926
00:50:03,500 --> 00:50:04,500
Yeah.

927
00:50:04,500 --> 00:50:05,500
Sounds good. Thank you so much.

928
00:50:05,500 --> 00:50:06,500
Amazing.

929
00:50:06,500 --> 00:50:07,500
All right, guys, appreciate it.

930
00:50:07,500 --> 00:50:10,500
I'm going to jump off for another poll.

931
00:50:10,500 --> 00:50:11,500
I appreciate the question.

932
00:50:11,500 --> 00:50:12,500
Yeah.

933
00:50:12,500 --> 00:50:15,500
As usual, send me any messages if there are any questions.

934
00:50:15,500 --> 00:50:16,500
All right, guys.

935
00:50:16,500 --> 00:50:20,500
Thank you very much, Turkey. Okay. Thank you. Thank you.