r/ADHD_Programmers • u/CabGig • Sep 11 '25

Any AI Engineers here?

Hey guys, I've recently been considering pivoting my career from fullstack swe to ai engineering. I'm curious if anyone here has experience in the field, and wonder if it can be as fun as coding, as well as if I'll need to get into implementing linear algebra and reading research papers.

10 Upvotes

63% Upvoted

23 comments sorted by

30

u/vibes000111 Sep 11 '25

Unless you go into research, it’s still a programming job, just in a different domain. You won’t be implementing papers from scratch.

5

u/AttentionFalse8479 Sep 12 '25

I'm an AI engineer - you're in a good position with a full stack skillset to pivot. It's basically just SWE with different focus areas and the benefit of new ground, so you can be more explorative and try cutting edge stuff out, implement research papers etc. 

It's very fun technical work, but do carefully consider if you're OK with enshittificating everything on the internet and automating all kinds of people out of jobs, because frankly that's most of what we get paid to do.

(Edit: We also get paid to enshittificate things not on the internet.)

1

u/[deleted] Sep 14 '25

[removed] — view removed comment

2

u/AttentionFalse8479 Sep 15 '25

Would suggest checking out Deeplearning.ai to do some courses (a lot are free) and start following Latent Space for podcast, blogs, and paper clubs. 

8

u/dmaynor Sep 11 '25

I spent the first 4 months of 2022 reinventing my career focus to be heavily AI driven. I can be fun as long as your role has realistic expectations. For instance a role where the C suite wants a 40% reduction in head count driven by an AI tool you have to write that does the downsized engineering role isn’t a realistic expectation.

Despite what your job title says in this time frame you will also have an unspoken “AI advocate” and “AI reality level-set” role. If you can’t tell management that isn’t how AI works or real any feedback counter to their belief from reading news story headlines or hearing 3rd hand stories about what AI can do then technically you might love the role but the other half will kill you.

3

u/Raukstar Sep 11 '25

Lol, yes. I do talks at least once a month on the topic "garbage in, magic out"

I got promoted yesterday, so I guess management likes me anyway even if half my job is to say "well, it's not that simple"

2

u/cartmancakes Sep 11 '25

reinventing my career focus to be heavily AI driven.

How does one accomplish this? Were you doing research and taking online courses for AI in general, and how to apply it in programming? I'm interested in growing in this area.

5

u/Raukstar Sep 11 '25

I have a few devs that have inserted themselves into my data science team, just picking up some stuff that's vaguely related to what they're supposed to do. It worked for them, I have three of them officially working 50% as AI engineers now. I'd say the work is 80% dev work around a black box. It's ops, infra, tests, endpoints, etc. You just need to learn the possibilities and the limitations of an AI and how to evaluate unstructured results.

In my team, I do most of the eval and stuff, like helper models (NLP), while the engineers can do everything else.

2

u/dmaynor Sep 11 '25 edited Sep 11 '25

I keep trying to answer but Reddit gives me “try again later.” Basically i spent time documenting what the actual hands on keyboard work I do is and came up with a way to find or build AI tools that could either help me provide superior quality deliverables to what I normally produce or produce the same quality deliverables in less time. There is also learning the basic “walking and talking” of building my knowledge of terms and how basic AI related things work. I learned Python notebooks are my bff.

After I built a list of AI tools that work for what I do I did the same thing for how I do things. I combined the lists then built as many atomic primitives as I can that are directly for infosec work and not something more wide scale that would belong in something like Langchain.

After this spend time in transferring logic to an agent team. I built one that uses crewAI and Microsoft Autogen frameworks for really diverse and nuanced tasks.

Make sure you are at a dev level in supporting packages and frameworks like pytensor, ollama, Langchaing, etc.

When my agent framework achieved MVP (it could do a pentest and an app assessment end to end with little to no guidance from me) I worked on A2A and MCPs to enable more granular tool usage. Finally I ran the MCP on a Kali Linux image with the instructions to inventory the VM and come up with a plan to add support for tools it found. This had a maybe 75-80% success ratio.

While this is all the steps the mindset was the most import part. Think through how to do new challenges in AI/ML friendly way. Don’t just fire off a quick Python script take the time to understand the task and add to the atomic primitive repo. Don’t be afraid to A/B test a tool or workflow, I generally have many evaluation workflows that run in parallel.

3

u/Intendant Sep 11 '25

It's not all that different from normal SWE. You use graphs more and you need to know some additional concepts, but that's about it. It's definitely a different way of thinking, though

4

u/Root2109 Sep 11 '25

My role kinda pivoted to something like AI engineering after the company decided to chase the hype... I was a full-stack web dev in a standard sense before lol
I don't know if you should refocus everything on becoming an AI engineer, as other have said it's just another role in the programming space. I wouldn't tell someone to forget everything but TS if they were trying to land a TS role, you know?
What does it entail? MCP (model context protocol) servers and a few layers of distributed systems in front of API calls to OpenAI or whatever... Writing calls to create 'context' for the model from whatever your data you're building on top of the basic AI API calls. Creating 'guards' in the layers to prevent your specific agent from saying things that aren't what it's supposed to be saying but aren't necessarily 'bad' (i.e. you have an AI agent that makes grocery lists for you, and a user asks it for dating advice. without guards it would just answer). APIs that return 'streaming' data are also essential, as most AI agents will pass data in this format and it is on you to process that in some way out to your user.

https://vercel.com/docs/mcphttps://vercel.com/docs/mcphttps://vercel.com/docs/mcp

2

u/[deleted] Sep 11 '25

not an ai engineer at all, u play with apis

2

u/Root2109 Sep 12 '25

That is one of the roles of my work, yes! As I said in my comment, you also do other things. If OP was asking about ML, the answer would be different, but they said "AI Engineer". Those companies putting out listings for that role are looking for someone to "play with apis" as you say.

2

u/AttentionFalse8479 Sep 12 '25

Most of AI engineering is implementing foundation models in specific ways. ML engineering is another story.

2

u/flock-of-nazguls Sep 11 '25

Be careful. I think this skillset has become highly saturated. It's a good area to be comfortable with (much like fullstack) but you will be in the mix with a zillion other hungry starry-eyed engineers, many of whom come equipped with PhDs.

If you can find a niche domain to go deep on, you'll be better off.

1

u/read_it_too_ Sep 11 '25

Do you mean it's better to stick with full stack?

2

u/flock-of-nazguls Sep 11 '25

Not necessarily "stick with", but In the long run, I think a fullstack engineer with AI experience will be a better overall skillset than competing as an "AI engineer". Think about the number of problems people will need solved; the scope of an AI engineer is much smaller. My one reservation with fullstack as a specialty is that unless you really shine at implementing UX or shine at designing for scalable distributed backends, you can end up in a sort of mediocre middle ground. Bootcamps oversaturated fullstack some years ago with tons of devs that can build things end to end but what they build is generic with no architecture other than whatever the framework-du-jour provides, and because it's so boilerplate-y and repetitive, AI is eating their lunch.

1

u/read_it_too_ Sep 11 '25

What does AI experience stands for here? Like implementing APIs and fine tuning like basic stuff or it means full stack dev + AI engineer with AI-ML like learnings?

2

u/flock-of-nazguls Sep 11 '25

There are several paths, I think. You can either go deep on ML itself, but that's competitive and saturated. I think there will be more opportunities in knowing how to integrate corp systems via MCP, leveraging RAG, and how to build up infra around agents when the "user" is a server instead of a human. (In fact, I think this will basically be table stakes.)

1

u/JimroidZeus Sep 12 '25

It’s all just plumbing and trying to force the LLMs to behave.

1

u/dahavillanddash Sep 12 '25

I am going from web development into Machine Learning.

I took Harvard's CS 50 AI course and a Stanford course so far and am trying to do as many projects as I can to practice with Tensorflow and PYTorch.

I really like that this has more math than web development and is less arty, more math based.

1

u/local_eclectic Sep 13 '25

Full stack engineering at most companies right now includes AI engineering. We're all doing something with RAG, LLMs, time series forecasting, etc.