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.