Forward Deployed Engineer: The Role Up 800% (And How to Get It)

Q12025 FDA rolls increase from around 800% to around 1000%. There 6 to 70% of an AI project now depends on adoption, not just engineering and and coding, but being able to. Co innovate with customers, Adaptability is very important, leadership becomes very important and the soft skills as well. Being able to rapidly iterate and deploy it is a career that is definitely worth to go ahead.

With. Forward Deployed Engineer, one of the hottest jobs in AI right now, with hiring up 800% this year alone, and it doesn't look like it's slowing down. We discussed exactly why that is, what the skills are that are needed, and why you should care if you're a software engineer right now more than ever. Joining me today is MOFA Gear, Principal Technical Consultant over at ServiceNow, specifically in AI, and he's built and shaped many forward deployed engineering teams. So enjoy.

Are you currently a forward deployed engineer? Because I I looked at your resume, I looked at LinkedIn, but it it doesn't state explicitly indeed forward deployed engineer. Yes, I'm not myself a forward deployed engineer, but I shaped and and helped shape the teams of forward deployed engineers, whether it's at service now and at many other customers there as well here.

The reason being again, like in terms of the birth of, of the role and, and how it started. If you look at the trends in, in the market there, the market used to go ahead and, and value four years of, of the skills and, and universities that used to be the, the economic marketing or power signal There itself, 22% of the premium was set on, on a master's degree. But the market now is shifting away from that.

And we already see that in terms of AI being at a much higher premium at, in, in current rates. Not only that, if you look at it overall in terms of software engineering, Q12023 to Q12025, it already dropped by around 70%. You'll get it on. On the other side, if you just take from last year to this year as FDA rolls increase from around 800% to around 1000% there. So that boom and that rise in AI mainly been already recognised adoption is one of the the main

bottlenecks there. It's not about the the capability there anymore. So helping customers rewire that entire their entire workflows and all their their systems to this AI native future is absolutely paramount. And as engineers, that is that evolution to that FDS is absolutely paramount there as well.

With all the new skills that are are coming about understanding the the end to end cycle of taking data-driven frameworks, starting the integration, being able to do easily take into account all the different systems, put that or bring about one data there in order to be able to to get the value of AI and expand it across the organization.

Why can't customers do the implementation when it comes to AI or tooling themselves in house necessarily? Now, in terms of AI as, as we know it has boomed significantly and only that the skills, if you look at the talent war itself in, in terms of AI between all the big organizations there itself, there are limited people who understand AI very deeply there itself only that number 2, not only do you need the the the skill sets that a person who understands AI very well there

as well. We need someone who understands all the system architecture as well as all the innings in terms of of the company there. And when I bring those two together, that's what leads to to success part that is is missing. There is essentially the skills and the talent to be able first to deploy of the the talent there is actually with the the frontier labs or with the the big organizations there. So that's where it's very important to help those organizations to be able to to

take that next step there. In terms of adoption from just the capability side, where forward deployed engineers are are becoming absolutely paramount. We've seen this start with volunteer from there go across the the ecosystem, as you've noted, now one of the hottest jobs in in the market as in terms of of the role there itself and like customers, especially now, yes, I have a capability and AII can take it off the shelf.

But these off the shelf solutions, many a times you realise they they still need to I still need to do some aspect or need to tune it in order to be able to, to get to success. And that at whether it's at at service now or or majority of our customers, we are we do have the market leading products and you can take it and already in terms of of deployment there we

have reduced it significantly. But what we have we have realised is if we do have forward deployed engineers sitting with customers Co innovating, understanding they already understand the product very well. But then I need the customer side of it and being able to go ahead and understand their entire architecture when I have someone sitting there with them Co innovating, that's what takes

them to to that success there. So the ROI in terms of AI projects there is significantly taken is much higher in terms of success when versus I compare it to to other projects when I have F DS also involved there. That skill set like I, I noted is currently limited in in DD market with this innovation and with this evolution to of the the, the role there, that is what is going to to take the market to to the next level

there as well. Yeah, While looking into for deployed engineers, I currently work at a consultancy. So I already go to clients and I do custom software development,

high code, very much understanding customer base, business constraints, technical constraints, existing landscape or going from zero to 1. Those are kind of the the various scenes that I've been in. So I already have to be adaptable. And then I read what 4 deployed engineers are doing. It's very similar to what I'm doing right now. So I'm strongly considering going and taking that step.

But for people that are listening, mainly software engineers or people in tech, what would you advise them if they are considering this? Is it a feasible career path in general? A. 100%, very, very good question. And yes, so when I look at the, the software engineering 1st role itself, we've already started seeing an evolution of that software engineering role

itself. Previously all the, the mundane, those tasks in terms of just coding it or, or being able to code, right, unit tests, all of those are augmented away now by AI. We've already seen that in, in the market as well. The market values, like I already noted, AI skills much more than than just now I'm looking at, rather than looking at the, the experience of the person, I'm actually looking at the skills that they, they have. And that's what like I noted, the, the market is evaluating

much more. So we take it from that perspective, evolution of the engineers rather than just like doing those, those mundane tasks, being able to do the strategic validation, being able to to go ahead and look at an end to end system integration, adaptable, not just engineering and and coding, but being able, like I know to Co innovate with customers. So adaptability is very important, leadership becomes very important and the soft

skills as well. So yes, in terms of the skill set on the technical side of it, we evolved there from just the, the typical tasks that we, we used to do there into a higher level where I can validate, I can do QEI can go ahead integration, which was the, the failure of most software projects as well and and getting the ROI with them. That again is is absolutely paramount. But again, also on the the soft skills as well.

Are very very. Critical for me to go ahead and and be able to derive and work with the customer to get the success and objections they're looking for. Yeah, when looking into the skills, I just thought, man, there are a lot of skills that are fitted in this role in this

person. On the technical side, I read since it is very much driven by AI adoption like Python, TypeScript is a lot with regards to front end frameworks, which also means you need to know your front end frameworks. So front end and back end specifically, you need to be able to deploy. So cloud native technology, whether it be AWS, Google Cloud or Azure in this case where you

deploy or how you deploy. So Docker and Kubernetes you need to be aware of. And then you have everything with regards to AI. So not just calling APIs because a lot of people can call an API, but what does it mean to embed a model or a feature within the product specifically? Or how do you build up and orchestrate agentic systems like that? From a technical level, it feels like a lot of depth that you need.

Need I could initially be a bit scary, but again, in terms of of taking it in into an approach there with the the skill set and one already has typically the hard skills that that are required. But essentially the evolution towards most of of the folks already know about multi cloud technologies about the container orchestration.

So that typically people have then the the more important side of it becomes whether I look at it, it's an end to end scale there not I'm really looking at it from the scope of just the engineering or infrastructure side of it, but an end to end approach. So when I say end to end, I start what is important there in terms of an AI project, there is not looking at it that at an entire scale there as well taking use cases, high value use cases to production is is very

important. So when we take it from, from that approach as well, understanding the ability of the system, analysing it and the data science in order to evaluate, find those, those high value use cases and then accordingly be able to, to translate that from, from a POC all the way to from zero to 1 essentially.

So when we take all those, those skills together, like you noted, whether it's the Python, TypeScript, the front end side of it as well, and likewise in terms of the back end, but being able to rapidly iterate and deploy is something very

important. So when we take it as as an approach there learning and being on the job on doing is very important rather than just taking like the previous approach of like noted, A4 year university degree as, as that is completely changed in terms of what the market values.

So when I, I take back end, front end, those are absolutely paramount there as well, like integrating into to AP is that again, is, is one part of it, but it's not just about engineering, consulting or in, in terms of such a role. It's about architecting an entire transformative ecosystem

with that customer. So when I take it from from that approach, starting with those high value potential use cases that can get a customer to success and then accordingly being able to, to iterate and then scale it out from there.

So learning how to pinpoint find those high value use cases that a customer can that can deploy or can get a customer to value. And then again, we can also as an early career or even even well had been advanced in in a software engineering role, looking to to trance or move ahead to, to the FTE path as

well. Here again, taking being able to take the data science part, understanding it, process mining, bringing the organization or what are the processes right now, the workflows that I have there, being able to again rewire those all to the the engineering age or rather to the AIH there. Those are all again, paramount concepts there to do so I can order or to simplify it and start with the data science and understanding the use cases.

Yes, again, the hard, the hard skills, the engineering part are again absolutely paramount here. But then finally being able. To to rapidly. Iterate and deploy that is is very important. So you take hard skills, typically we all as as engineers, we'll all have those.

Then to be able to rapidly iterate to have that sense of urgency and then not only think about it from my engineering scope, architecting and that transformative, transformative ecosystem through product centric thinking to that data science and data-driven frameworks is what will will lead the person to the next spot.

There could be scary initially, but definitely if you get once people get their their hands dirty and it's as a quite a career part for for a person as I have done in in terms of service now as well. I think the reason why I feel like it's daunting is because when I look at FD ES and kind of the skills that are required, there's a certain level of depth on a lot of various aspects, right?

Being good in one thing when it comes to T shaped profiles or being good at 2 specific things when it comes to pie shared profiles. That is no longer what I feel like is there in for deployed engineers. And the daunting part is they are asking that of you. But in practice do FD ES? Like are they solo operators at customers or they typically work in teams? Again, here look, it is in teams typically in terms of FDS.

So for instance, if I take the, the model of majority of, of enterprises, whether including Palantir, ServiceNow, Open AI and many others, there's forward deployed solution architects, there's forward deployed product managers as well as the, the engineers there as well. So it's a make up of a small nimble team that actually works together in order to, to get that customer to, to success. So it's not just a, a solo operator, but then again, that leadership side of it is, is

absolutely paramount. Although it's a small nimble team, being able, like I noted, to work with that higher urgency and get that custom to success is, is absolutely paramount there as well. Take an AI project and compare it to to other projects there around 60% of 6 to 70% of an AI project. Now depends the success of it depends on adoption, 10 to 15, around 10 to 15% depends on on

governance compliance. So if I take those compare it to a software engineering project, a typical project is much different. So hence the the the forward deployed engineer and the importance of them becomes very, very much more as I compared to to a previous one. But they're not solo operators. They work in, in small teams. Solution architects can bring that expertise in terms of looking at it from the solution side that are we already have there. And then from the from the our

customer side. How can I architect an an end to end system looking at it from the responsibility side, governance side, compliance side. And likewise, indeed, there is the engineer as well. Yes, engineer. Now if I compare it again, like you noted, could be daunting in the sense of AT shaped is, is is very important. So still having want the Bretton and Depton and the engineering side of it.

But Gandhi, the product centric side of it, how I can go from zero to 1 and, and the design UX and all the elements around that being able likewise to take that from in terms of the infrastructure side of it as well. Understanding the the models, the usage that again is, is absolutely paramount there with, with the the use cases, the ROI side of it, then that's where the ROI value alignment is, is

absolutely paramount. Those areas bring them together the solution architecture engineer as well as EDPMS. That's what can can get again those those success. So like noted, it's not completely dependent on on DD engineer, but the skills, yes, the skills needed are more than than what was typically needed or or valued previously.

Rather than the the writing of all of that code, way I can generate all of that code there be able rather to being able to have that, that validation capabilities be able to to easily take that identifying security issues with the Edu code there and then accordingly take that to to production. It's absolute paramount in terms of skill sets. This sounds like a lot of fun, I must say, because I'm a person that likes ownership and impact specifically.

And I feel like as forward deployed engineer, you come in and the customer probably has a feeling of what problem they have and you might identify that's the right problem. Or you solve a different problem, actually an underlying problem that they didn't even know they had. And you do that and you come in hopefully then with a team that you feel more secure in like in the one side I I get super

excited. On the other hand, I always have that feeling of imposter syndrome because there's a lot and high stakes here. I feel like when you are an FTE,

when I came into CBS specifically, I was very early in career and the only reason I felt comfortable was because I knew I was always going to be in a team. There was a setting where my part of the company was always

working in team settings. So then not like individual consultants going to clients, I always had the security of people around me and especially being early in career, that was what gave me the confidence to also learn and grow and make mistakes and continuously actually do this. I'm wondering in an FDE role already right now on the market, there's very little junior hiring. People are still looking at very much media and even more

seniors. But you're saying that for juniors or people early in career specifically, the FDE path might actually be a really realistic one as well? Certainly, certainly so very similar to to your story as well myself. I, I started that at ServiceNow three years ago as as just an internal associate consultant there and in the AI field there as well.

Was able to work on on the job, like you've noted, be able to to get the support of of many great individuals and and teams at at ServiceNow to help me grow, work with customers and then innovate and grow to where I'm today as as an AI lead at, at ServiceNow there. So with that, the ability of understanding, so even at an an early in in career phase, having those those skill sets, the market also values that those those folks early in career are already AI native.

They have from a very early age of already started working with those capabilities and that's what even research their shows. So with that, yes, although the there is early in career, the, the, the hiring has reduced their that should still not shy people away from from the the role there because like you noted, there's a lot of excitement and value that can be obtained with it AI this the pressure is, is is much higher there in terms of an AI investment into a project or in

a on a product is higher. So the ROI expected there and the time to value is, is on different level to a software project. But again, the, the working in those, the learning side of it, the challenge that one is, is put out that it is the pace of, of growth and learning is much farther and rapid than any other role to, to, to my experience, or at least in, in my opinion, that shapes or grows the person

very quickly there as well. So that I that's why I highly recommend DD Field there being on the job, being challenged every day with that pressure there as well to to deliver value something that is there, but at the same time brings the best out of a person there as well. I'm wondering both for people that are early in career or media or senior Indian, doesn't matter, how do I get in? How do I in the end secure a position?

As for deployed engineer, what are the skills that are measured? Maybe even through the interviewing process or or when you're forming A-Team, what do you look for? Sure. If we, we take we talked about the the hard skills there as well. So if you look at the back end or front end, the multi cloud technologies, the AWSGCP, those are all again taken in or required as as part of the role. And then you take it into the AI side as well. I mean, there's so many ways to go ahead.

And today, just if you, if you look at the ability to just take over an AI project online, I can take an API taking and they're start building out something that that is quite transformational there as well. I mean, there's even apps, for instance, like Replict and I can easily wrap prototype as well develop and from zero to an entire website. So all those, those skill sets are again, very important.

Being able to iterate and, and learn and show that you have those, those experiences and those projects that you have developed are absolutely important there as well. So that's on the the hard skills. But if you look at it from the soft skills, again, that is very, very important. So typically on the the engineering side, the soft skills, I mean we're valued more in terms of, of the, the, the codes as well as the products

there that, that we ship. But the soft skills becomes much more important in terms of an FTE role as well. So like I noted in terms of the, the value of of and the ROI of these products are quite massive for the organization's they're in transformational value that I can bring about. So at the same time, the, the soft skills, being able to lead that to success becomes important being adaptable in terms of pure, I have an A challenge environment.

I need to I need to develop and take something to, to production at at speed there as well. It's been adaptable. Working with the customer and Co innovating with them is, is very important, but also being a leader, being able to innovate, bringing that visionary thinking is are all skills that that are important there as well. Obviously collaboration and communication, partnering with that that customer to be able

to, to take them to success. Those are are the skill sets that are important, but to simply be able to take that next step, I would say first step, just get your hands dirty with with with AI there. There's so much that is out there today with AI and it's very early, very early innings

for for many people there. So I've been, if you've not yet started there, whether you're early in career, whether you're advanced in, in your career there, there's that opportunity to just take those, those steps and projects online, do it. And then from there, when you're able to show it, then that's what the, the market values now as well.

Like, you know, we are, we're moving to a skills based economy and we see that already in the biggest in, in the biggest enterprises and, and also how the, the, the global market is, is moving. So moving away from just experience to skills based. So if I'm looking at those

skills based, being able to to learn, prove and show my skills is much more important than than just having those. Those whether it's any certifications or anything else, those are great obviously, and they add a lot very important. But at the same time, showing and doing is is more important

there as well. Yeah, showing and doing, I feel like is is never been as important as right now because I mean, especially when people are trailblazing when it comes to developing a product that has an AI functionality or even building a small agentic system for whatever it is. I was talking to someone and she said, I have three boys and now they're at an age where they're all going to tennis tournaments and they have judo and they have swimming and I need to sign them

up for multiple things. And she just vibe coded some some agents that do that for her that go through a lot of websites, give her a summary and then she can execute or then sometimes even execute all in an automated way. I was like, that's really cool. It's in essence. In essence, it doesn't necessarily matter what it is. I feel like what I want to see is passionate and I don't want to see people that wait, but I want to see people that have already started and I can show me something.

Exactly, exactly. Very much so it's, it's about being able, I mean, there's being being able to even in terms of just generally A-Team and, and building an, an AI native. I'm looking at an AI native team there. The skill set is, is absolutely paramount and and important there, but the attitudes is, is even more important there. The ability to be passionate, have that, that urgency to want to learn, want to do more is very, very paramount there as well.

And that can easily be shown through those projects and like, you know, that even vibe coding those, those those as possible today, even if I have zero to to no experience, that again, is something to to that can easily be done with these AI capability. I can generate an entire website and an entire vibe coded start from zero to 1 iterate. And with that, I can Start learning and and developing the parts of, of my understanding of those capabilities.

That essentially is what the market and and enterprises value a lot today. From your perspective, attitude, mindset, sense of urgency, are those things that people just have innately or can they be trained?

And I definitely think those can be trained. It's #1 not only on in terms of the person, if I take it, for instance, even with those at, at ServiceNow and, and the team, they're the, if we have, if there's one I need, if we have a strong leader, that with a vision. And then that I'll first talk about it from the leadership perspective and then from, from a person itself. But if you have a strong leader with a vision that can go ahead.

And if everyone believes in the mission and, and the vision, things that you can achieve are, are limitless. The potential is limitless. And that again comes to, to a person there as well. If I can lead myself similar to to what happens in in a team, if I have a mission, if I have a vision that I deeply believe in there as well. And that essentially is what can help me iterate and and go further. How can I train myself or build that in terms of of a muscle, just just like muscle memory, I

need to build that. Yes, it doesn't come over a day or or a month, but I need to build that in terms of of a muscle there and train it. How can I train it and, and build it? So just what I believe in and the most important thing is writing things down and here whenever I whether it's a one year plan or it's three-year plan, five year plan, just write down whatever you think. Where are you today? Where could you be tomorrow?

And then in terms of of that tomorrow, do not judge yourself against today, but judge whenever you have the ability or something, any action, a decision that you need to take.

Ask yourself what would that tomorrow version of food of me would would take the OR what that tomorrow version take in terms of a decision and accordingly take that or or start shaping yourself and thinking to that as well that training, training it over time has really helped me there and be a better person be shaping that urgency that that passion in me there as well. Just noting things down having doesn't even have to be a very clear plan, but just a plan.

Where am I today? What am I? Where could I be tomorrow? And then judging myself by the tomorrow in terms of every decision is is a big value and then training it in in terms of muscle. And then similar to that in terms of of leaders there as well. Leaders and in in organisations very important again have strong vision, a mission for my team.

And then based on that, a team strongly believes in in that mission and that vision limitless potential that that can be achieved accordingly there. I like that a lot, acting like future you would or should and already doing it instead of waiting until you get there. Exactly, exactly. And sure as well it changes DD perspective from from judging yourself into which again is something that could potentially put in in terms. Of a psychology side of it. Put people off.

You're taking it to what could that other person do? Which is essentially or the other version of me would do. And then accordingly, I would, I would take that, that action as well. So psychologically could also help or or influence a person to do better. So that again is is how we've taken it over, at least for myself, taking it over time and and shape the person I am in terms of my urgency, my mission, my my passion and my beliefs

are. Yeah, when you were talking about some of the responsibilities of a for deployed engineer and especially the ones on the soft skill side. I've worked in teams where people are just not happy talking to stakeholders or PMS or being in what they call meetings and everything is a meeting. And sometimes back-to-back would just actually absolutely drain them. And I feel like those people are not well suited for the forward deployed engineer rule.

I feel like interactions with people, having tough conversations and getting by and and making sure that you execute or you're apartment and enabled and equipped to execute, that's a big part of it. So then I see 2 clear camps, the people that love let's say the outcome of things and also the interaction with people. And then people that love tech for the tech where it's mainly a craft for them as well specifically. And then one camp can become for

deployed engineers and 11 camp. I don't think can and I don't know if they should. That's a a very good point. And indeed, like I said earlier, the soft skills of a typical software engineer to what is required as as a forward deployed engineer, yes, they are quite different.

They're I'm expected to to ship products to to write codes and. Sure. It becomes quite different. We're working together Co innovating with that customer is absolutely paramount there as well. I mean like noted in terms of of AI use cases and the investments in terms of these projects, they're significant. So in terms of taking these two to success again is is also very important there as well. So with that comes different level sense of pressure, urgency and all those those areas.

So yes, being able to communicate, be in pressure, be a leader and be able to, to take that to, to successes, be driven to take that to, to successes is important. And yeah, even in, in those pressure environments, those communicating with people, those are again, if it's not innately in, in, in a person, then those, those do need to to be

developed. I mean, if I take it from my perspective as well, I'm actually an an introverted person, but I started at NASA when I was a researcher and AI researcher initially in in my career. I would much prefer to just sit behind the laptop, call out, innovate, do research and I'm. Quite a happy. Person and then what changed me essentially I took a sales role, challenged myself, took a a sales role in in the UK. There it was door to door used to do engineering.

It was internship engineering in the morning, then in the evening used to do door to door sales. Proper sales. Proper sales, door to door. And then there's, you literally have, we have something called law of averages, which is the number of people you need to speak to before you you can make a sale sale essentially. So the averages usually averages to three to five people that you sell when you speak to 100 people. So. Oh, wow. Exactly.

Yeah. So imagine knocking 100 doors at the minimum a day in order to make two to five sales and you get around 95 to 97 rejections a day. So that's quite it shapes up or change the person quite a lot and that is essentially what would got me or help me get get

myself out of my shell. So I would say yes, although the person could and for me, I still today prefer to, to be introvert, but that definitely changed how I communicate, how I interact with people, how I can easily or be able to, to speak to to anyone, accommodate to a situation and and be able to be comfortable with being my myself and sharing my ideas there as well. So that I think although it's not in Italy there it can be can be trained or got in terms of of

a person there as well. Yeah, I feel like you've truly put in the reps. Like you don't just get where you you want to be. You have to put in reps and you do that day in, day out, and that's how you grow innocence. You mentioned NASA. I want to do a quick detour because all the people that I spoke to where I said I'm going to talk to MO, he's been to NASA for an internship. Everyone's like, whoa, how cool is NASA from behind the scenes, from your experience it? Was really, really cool.

So I went there early and I was only 16 when when I went there, but it was really interesting and, and what's pinned up my passion and, and AI there as well.

So essentially I went there and started as was, it was an exchange program where we're essentially researching about renewable energy, how we can optimize those systems and how we can use AI to that as well. Here solar panels was the main study and there how we can use AI to prove it was was again a big area amazing experience again in terms of working with some of the greatest minds that in the world that you have there the access there and then be able to to help the field and

and move the field ahead. As was essentially how what got me like I noted in all of this, and it was amazing as as an experience at was at Kennedy Space Center in in Florida. Again, beautiful place as well. And essentially partly in there's one side of NASA there as well in just a reserve. So again, normally do you can you work with the greatest minds, but you also have just animals that are roaming free as

well around. It's just beautiful scenes, beautiful mud place people to or beautiful places as well as amazing people to, to work with there as well. I did stay or there around around a year before I, I moved, I had to, to the UK and again, was in the research side of it of, of AI there. And again, look at how we can apply AI, whether it's in in Kenya and and other places to, to optimise their end to end electricity provisioning systems and, and improve that. And again, that's essentially

working in all. One thing about AI, which I love was is how, whether at NASA or all these different places, you can apply it in every fields. You can apply it in every field and it can bring a lot of value there as well.

That's again, another reason why if you're looking at it from a perspective of software engineer, what can be very appealing, Yes, even engineering, I can essentially create an app or develop a, a solution for in any fields and, and to support it. But again, that is further augmented and further expanded when when it comes to AI there as well. That's again something I learnt

very early at at NASA as well. The ability I can apply AI in anything and it's, it's something that can potentially get get transformational value. Obviously, at that time, it wasn't as as big or as around 810 years ago. So it was, it's AI wasn't as as big today at all as as it is. But still at that time there were early innings and it was clear that it was big potential in in the field there as well.

Yeah. One of the things that you mentioned specifically the reason why for deployed engineering jobs are booming and growing and hiring specifically and also people applying to them is because we are trailblazing with new technologies where companies and a lot of tools are out there. I mean as a software engineer, I

get bombarded with tooling and from a marketing perspective mainly. So then actually applying it in a production level system, specifically enterprise but also user facing, that's the challenge. And that's where for deployed engineers will help. But then does that mean that when this technology becomes a little bit more stable and people are a little bit more apartment with what they do, that for deployed engineer jobs will go down or will be reduced or will go the knowledge will go

to organizations themselves? How do you see that? Great question, SO. Number one in terms of AIAI is still very, very early and I think it's early innings. So I mean, you hear in the market these days, is there a bubble? So I'd I'd say there's absolutely no bubble at all because it's very, very early innings #1 if you look at AI is. Bigger than all the previous.

Whether you take the Internet, whether you take mobile clouds, combine them together, you know is is bigger than them in terms of the, the opportunity there #2 why is it early innings? If if you look at the entire Internet and how it is commoditized, the app, the ads and all of that, that's all

today done. The recommended systems that's all done today through, through just the, the machine learning workloads there all that needs to be taken away, architected and transitioned into generative AI workloads. Take companies, every company in the world essentially that needs to, to get that edge and and compete. There is already adopting AI And today we see it moving already from mainly from PO CS to, to production and, and they're

scaling out their usage. So it's very, very early innings for AI and, and the opportunity there. So in terms of the, the, the, the potential rise of, of the role, it's, it's going, I'm pretty sure it's going to be quite exponential for, for the coming decades there to when you look at EI itself and the pace of, of innovation beats any other technology that are around as well.

Now we have models that are coming about every other day, whether if, if you look at it from the, the closed source or even open source perspective, just DeepSeek just released a new model there as well, new in terms of new paradigms that it uses in the RL side of it as well.

So we can still see not only that there is potential in terms of scaling when I when you look at it in the, the RLM side, there's there's potential for scaling in the pre training, There's potential for scaling in the, the post training aside of it. And there's also the, the potential for still new recipes, new innovations and inventions that can help or help take the, the AI to the, to the next step there as well to general and, and super intelligence.

So opportunity again with AI, not really is it huge for, for the the entire market and still earnings, but self AI field is, is rapidly innovating and the pace is, is much faster. So you put those two together is is it going is, is there potential for it to to decrease or or plateau? I don't see that potentially for for the next decade or so until and when we get to general and super intelligence in in the the

next decade. And you can imagine every organization our have the potential to go ahead and create new products to do discovery, drug discovery and and medical discovery. There I'm able to to go ahead and transform an entire organization and manage organizations autonomously. That is completely different paradigm there. And that again becomes absolutely paramount, even more paramount to have those those forward deployed engineers there as well.

People who innately understand those those products and are also are able to understand the the customer, the customers, the architecture, their systems and their processes in order to be able to to rewire them there again for for the future. And finally, as as a point in, in terms of AI today, there's, it's all mainly based were already based on, on today's current technologies, the likes of the GPUs and Nvidia's or, or any one of the the latest CMD and all the latest GPUs and, and

E6 there. But again, there's quantum computing that potentially can come about there as well. And just a few days back, IBM, there was a video on IBM and Bloomberg on, on the potential of, of their, their quantum computers there as well. Bring that field again into AI.

And there is even more rapid innovation and and iteration that can come into market when we have all these, these large scale AI factories that we're right now just building up for, for these fields is that I can again reshape at all. So I do not foresee a plateau at least in in the near future when I put put those all together. Yeah, One of the last thoughts I had, it brings me back to an experience that I had also quite early in career because I went to a training training to

actually demo a local platform. The training was to figure out how to then use that and implement that at customers. And at the time I decided I don't want to be a local

engineer. I felt like if I go this route in my career, I would be very much tied to a technology and yes, I would understand customer domains and I would implement, but my technical expertise would be tied to this one thing. And if that one thing dies, well I have to build up another technical capability from scratch. Do you see the same risk with forward deployed engineers? Like what makes people for deployed engineers different from Palantir versus service now with the technical expertise

they have? Now the hard skills if if we take those are typical across all organisations there as well. So if the multi cloud technologies, the the below it, the Pythons, the the type space, the you're taking it from the the container side of it, all of that is typical and and the same. I need to know all those hard skills is what I'm saying are are typically what are any organization that I go to, I will still need to go ahead and

apply those. What is different is essentially the product and how I can I can go ahead and take that, innovate it or or customize that according to to the customer objectives there. So what is more important is being adaptable in terms of of in the, the space of AI like I noted before, AI can be applied in any field. So whether I apply it in Palantir or ServiceNow or or any other organization, it's still going to be AI there.

And then in terms of the the agentic and the the latest innovations in market there with the capabilities, yes, there are differentiators. For instance, if we take it away at ServiceNow with, with our capabilities, if you take the gardener, the OSG reports we're market leading and we have we're number one for instance on, on gardeners building and managing

AI agent use cases. How are we able to to achieve that is can we have our, our, the ability to rapidly take not only just off the off the shelf solution, but I can go ahead take it or customize that very easily and and be able to to take it to production. Now those tweaks or those differentiators are going to be in, in every product or every enterprise that a person might, might potentially work in.

But that is it's essentially an add on, not the core part of or the course skill of of the person that they will have, especially in an engineering or or an AI field there as well. So I would say if one has like noted earlier, learn learning by doing and then showing versus just going to to take the traditional path and then accordingly building up those hard skills and being able to rapidly build those those up as

as well. And then third again, when once I have that the add on is essentially the the product to where I'm going to, to work on there as well. And I'm very privileged just to work at service now in order to be, to be able to, to do that for some of the leading enterprises in the world. Yeah, amazing.

I like that a lot. For people listening, they will now have a better understanding of what the forward deployed engineer is both in a role and execution, as well as the skills and potentially even how to get there.

Get a foot in the door. What is one last piece of advice you want to leave people off with? Wait, let's say. So we, we've talked about how the market and how it's rapidly changed. Like noted from if you take AQ one to QQ 123 to Q125, the software engineering jobs dropped by 70%, white collar by just around 36. If you take the AIFFD year olds eight, 800,000 or 800% to 1000%. There signal is very, very clear in the markets #2 what the market values is different

skills are much more important. So learning again, learning by doing and being able to, to show that is, is absolutely paramount there as well. And #3 it can, yes, it can be daunting as in in terms of what is or the the requirements there. But don't shy away as we talked about the value, the potential and the opportunity is significant and even like noted in terms of the could it potentially. Plateau.

With all those points that I I provided, there is little to to no chance in, in my belief there. Put those all together, it is a career that is definitely worth to go ahead with and into, especially in the the AI side of it and being able to combine it with that engineering take organisations forward. I feel again, challenging yourself and growing with the the ability of AI and where it can go, the opportunity, the opportunity of AI and where it

can go is quite significant. So take it start, don't try always is what I would say there. MO thank you so much, I really enjoyed this. It's been an absolute pleasure, Patrick. Thank you very much. Thank you. If you're still listening, let us know in the comments section what you think and we'll see you in the next episode.