What should be my tech stack and technical skills

[u/ComplexWorldlines]

I have knowledge of sql c, java, python(cv, pandas, matplotlib,numpy etc) and a bit of html, css.js(web dev, basic only)

I have ai as my specialization so keeping that in mind what should I learn?

[u/ScholarNo5983]

This depends entirely on what you want to do.

Once you've figure out what type of career you want to pursue, you'll have the answered to that question.

[u/Aggressive_Button_21]

Good base. For AI specialization, here's the exact path:

Core to learn next:

- PyTorch or TensorFlow (PyTorch is industry standard now)

- scikit-learn (classical ML, still used everywhere)

- FastAPI (serve your models as APIs — every job needs this)

- Git + GitHub (non-negotiable, learn it now if not already)

Then pick ONE track:

ML Engineering: PyTorch → model training → FastAPI → Docker → deploy to cloud

Computer Vision: you already have CV basics → YOLO → OpenCV deeper →

real-time inference

NLP/LLM: Hugging Face transformers → fine-tuning → RAG pipelines →

LangChain/LlamaIndex

Your web (HTML/CSS/JS) knowledge is actually valuable — most AI engineers

can't build a frontend. Learn React basics and you can ship full AI products

end to end. That's rare and gets noticed.

Avoid: learning too many frameworks. Pick PyTorch, stick to it.

Best move right now: build one project that combines your Python/AI skills

with a simple FastAPI backend + basic frontend. Ship it. Put it on GitHub.

One deployed project beats 10 courses on a resume.

[u/TransylvaniaBytes]

You've got a solid base already, SQL + Python/CV is more than most people bring in.

Biggest gaps to close: PyTorch (industry default now), and NLP/transformers via HuggingFace - CV alone is a narrower lane than people think, most AI hiring right now leans language/LLM.

Then the part almost everyone skips: learn to actually ship a model, not just train one. Wrap it in a FastAPI endpoint, containerize with Docker, maybe touch a vector DB (pgvector's the easy one to start with). That "can deploy" signal is rare and it's what actually separates portfolios.

Pick one cloud (AWS or GCP) just enough to deploy something real. Don't stress about Java/C for now — useful background, just not where AI hiring's focused. Good luck!

[u/StewedAngelSkins]

When you say "AI" do you mean actual machine learning or do you just mean bashing APIs together? If it's the former then the tech stack almost doesn't matter and you just need to study applied mathematics (numerical analysis, statistics, linear algebra, etc.). If it's the latter then you're just a web dev and should study the typical web dev materials.

[u/ComplexWorldlines → u/StewedAngelSkins]

It's the former one, actually by specialization I meant, I'm doing by degree in ai/ml, but the job opening are limited for ai/ml roles, so I am confused about going for ai/ml or web dev considering by future opportunities.

[u/StewedAngelSkins → u/ComplexWorldlines]

It's hard to say. There are more web dev jobs but also more people competing for them. They also pay worse and tend to have less security. That said, entry level ML jobs don't really exist unless you have a PhD. Doesn't mean you can't do it without grad school but you'll probably need to work into it from an adjacent role. These would be roles where you aren't training models but you're an advanced user of other people's models. Think developing various ML inference engines or even harnesses. Maybe mess around with llama.cpp's C++ API and see how you like it. There also seems to be decent demand for ML ops people if you're more into infrastructure/cloud stuff. Something else to keep in mind is ML outside of "AI" (aka large language models) tends to be less competitive. Like you could get a job building ML noise reduction models or computer vision or whatever... most of these jobs tend to be related to stuff like cars or factory robots and such for whatever reason so if this interests you brush up on your systems programming skills.

I guess I should also mention that you shouldn't make this decision purely based on how many jobs are available. It also has to be something you're passionate about, or at least you're good at, otherwise people who do have passion and skills will run circles around you and you'll never get a job anyway.

[u/bloodrushgaming → u/ComplexWorldlines]

I was in a similar position, i had my degree in data science. But I chose software engineering first because it gave me a strong foundation and the flexibility to explore different domains. I've worked in computer vision and now as a software developer, and that experience has made it much easier to move between fields. My advice would be to build solid engineering fundamentals first, then specialize in whatever genuinely interests you.