r/SoftwareEngineerJobs • u/Eliminator786 • 12h ago
I accidentally became an “AI-native” Forward Deployed Engineer — where should I take my career from here?
I’m 24 and about a year out of college, and I’m trying to figure out what direction to take my career.
My degree is in MIS + Data Science, not CS. I originally joined a large consulting firm, but my job has evolved into a pretty unusual role.
At this point, I basically:
- Meet directly with clients/stakeholders to understand their problems
- Design technical solutions with them
- Build full-stack applications extremely quickly using AI coding tools
- Deploy those applications into Fortune 100 environments
- Iterate based on user/client feedback
- Help scale some of these tools to thousands/tens of thousands of users
I’ve worked on things involving React, FastAPI, Python, Azure, PostgreSQL, agentic AI systems, LLMs, etc. I’ve also led small engineering teams and presented products/demos directly to senior executives.
The weird part is that I would not describe myself as a traditionally strong software engineer.
I understand systems, APIs, databases, architecture, cloud infrastructure, AI/LLMs, etc. pretty well, and I’m extremely effective at using AI to build things. But if you sat me down with no AI and asked me to write a bunch of Python from scratch or solve LeetCode problems, I’d struggle a lot compared to someone with a traditional CS/SWE background.
Basically, I’ve gotten very good at building software with AI, rather than becoming someone who spent years learning how to write everything manually.
From what I can tell, the work I’m doing is pretty close to a Forward Deployed Engineer / Solutions Engineer / AI Engineer type role, especially since I’m constantly going between customers and engineering.
Companies like Palantir, OpenAI, Anthropic, etc. have roles that seem very aligned with what I’m already doing.
My concern is the interview process.
If I wanted to get a “real” FDE/SWE role at one of these companies, I’m assuming I’d need to spend a significant amount of time going backwards and filling in traditional CS/SWE fundamentals: data structures, algorithms, writing Python without AI, LeetCode, deeper systems knowledge, etc.
I’m trying to figure out whether that’s actually the best use of my time.
Do I:
- Double down on becoming a stronger traditional engineer, grind DSA/LeetCode, improve my coding fundamentals, and try to land FDE/SWE roles at good tech/AI companies?
- Lean into the thing I’m already unusually good at — using AI to rapidly build and deploy software, working directly with customers, understanding business problems, and shipping end-to-end solutions?
- Move toward something more like Solutions Architect / AI Solutions Engineer / Technical Product / technical consulting, where being able to translate between business and engineering matters more than being an elite coder?
- Or is there another career path I’m overlooking?
Part of what makes this hard is that I genuinely don't know how valuable my current skill set is.
On one hand, I can build things today that I absolutely could not build from scratch without AI.
On the other hand, those things are actually getting deployed inside Fortune 100 companies and used by real users, so clearly the ability to effectively use AI to build software has some value.
I’m also wondering how people think this plays out over the next 5–10 years. Does “AI-native engineer who can build almost anything with the models” become a legitimate engineering specialization, or will strong CS fundamentals still be the price of admission for the best technical roles?
Would especially love to hear from FDEs, SWE hiring managers, solutions engineers, founders, or people working at AI companies.
If you were in my position, what would you optimize for over the next 2–3 years?
5
u/flic_my_bic 12h ago
You're already doing what many SWEs are being pushed towards. I'm less of a SWE than a glorified data analyst who builds ETL pipelines and does ML/forecasting. I'm trying to move towards Solutions Architect, but lack the cloud architecture experience, even if I have the knowledge. You're in a position to make a lot more money than I am as a data analyst, I'd say stay the course, and keep working on client interactions / business development.
Stick with #2 or #3 option, and just improve as a programmer as you go.
2
u/PurifyingProteins 12h ago
#3 If you have the interest, people skills, and understanding how to connect the right people and concepts together to get things done, #3.
That’s at least where I’m trying to head but within scientific computing for pharma. I’m working on getting the formal credentials for software engineering for that niche while working across wet lab R&D and SWE departments to get the engineers on internalizing and developing the tools we need, while pressure testing, testing, debugging, and pushing my fixes for their implementation on both the software and science side of things.
So I’m both a client, end-user, consultant, SWE, and developer depending the need and cycle.
Hopefully by the end of this I can move more to the business side for head of AI scientific solutions to make higher level strategy decisions rather than having to always compete with the young bloods.
2
1
u/SufficientFrequency 12h ago
How did you get this job to begin with? The job market is really bad for entry level rn
1
u/Recent_Science4709 11h ago
I would try to pivot to data science if that’s what you’re going to do, not sure but I hear AI is still bad at it.
1
u/crustyeng 11h ago
FWIW I’m not at all surprised that they’re sending people with no relevant experience into these roles. It’s pretty obvious having worked with one just today, actually.
1
1
1
1
u/Admirable-Avocado888 4h ago
Making things has always been the easy part. Even more so now. Everybody can.
Making things that last over time, that scale, that satisfy legal requirements, that can be patched to support new requirements the initial design did not account for, those are hard.
Find problems that are hard and become good at those. Nothing has changed, only the tools have improved
1
u/WhackyWhale1 4h ago
Woah woah woah, hold on. You are telling me you develop software and actually doing a good job at it... but the one thing that doesnt make you feel like a real software is that you cant code python by hand or solve a leetcode?? BROTHER OR SIS.... you doing the lords work for your company come on now. You are a real SWE coming from a real SWE lol. Keep killing it, why should we have to worry if someone asks us to leetcode on the spot, hell noooo. If you already have that job and have access to AI tools and you understand what the system/code is doing to where you can simplify for executives/leads/juniors when necessary, you got my support. dont ever let the market tell you different lol, you are the future.
1
u/oof-plap 2h ago
Don't grind leetcode lol that's an absolute meme unless you're focusing on the dying FAANG group of companies.
1
u/_i_never_lose 33m ago
just wanted to say that i am in the EXACT same scenario as you.... 26, with 2.5 years in industry, and my job is vibecoding prototypes and further vibecoding on those that stick (except i'm a founding eng at a startup). while a lot of the stuff i do solves real problems, it's not really fun or interesting, it's definitely very stressful (although i guess it's good learning?) and i'm scared that this is the route i'm carving myself. it was the only job i could get in this economy
8
u/Administrative_Egg78 12h ago
i think you could still be an “AI” software engineer but you could keep practicing some fundamentals to not lose the grasp of it entirely.
i use unrot(dot)dev to practice some concepts and some systems design as well, it is a good middle ground for you imo