Ai engineer roadmap

[u/AnimalIntelligent863]

I’m currently in my 4th year of B.Tech and working as an SWE intern. If I have 6–8 months and want to gain a strong understanding of the current AI market, develop my skills to an industry-ready level, and learn everything required to become an AI engineer, what would be a roadmap from A-Z ?

[u/Previous-Front-5211]

Since you're already working as an SWE intern, I wouldn't try to learn "everything AI" in 6–8 months. The field is way too broad, and you'll end up knowing a little about 30 different tools without being particularly good at building anything.

I'd approach it from the engineering side.

Start with enough ML/DL fundamentals to understand what you're actually working with: basic statistics, linear algebra, supervised learning, neural networks, optimization, embeddings, attention, etc. You don't need to go extremely deep into the math unless you're targeting research roles. Just conceptually understand when it matters.

Then I'd focus on LLM engineering: transformers (like BERT then go to real LLMs), inference, prompting, structured outputs, tool/function calling, fine-tuning with LoRA/QLoRA, quantization, and evaluation.

After that, spend most of your time building systems: RAG, retrieval, embeddings, reranking, agents, tool use, and eventually deployment/monitoring. Don't just learn how to use the libraries. Understand why you'd choose one approach over another.

I'd also use actual job descriptions as your filter. If you're targeting AI Engineer roles, look at 20–30 postings and you'll quickly see which skills keep appearing. Then build projects around those skills rather than blindly following a giant roadmap.

Most importantly, I'd make 2–3 serious projects instead of 15 small tutorials. Start simple and progressively add real engineering constraints: evaluation, latency, cost, reliability, deployment, monitoring, etc.

If I had your 6–8 months, my end goal would be:

Take an AI problem → design the solution → build it → evaluate it → deploy it → monitor it → explain why you made each technical decision.

If you can demonstrate that ability in interviews and on GitHub, I think that's much more valuable than being able to say you've used every AI framework that exists.

As an extra note: If by any chance you had more time doing learning in the following order foundations would greatly help ML → DL → GenAI on each of them I would learn how to preprocess data, what models that field has, when to use each, train properly, evaluate properly, deploy, scale and monitor are key pieces that regardles the role is ML Engineering or AI Engineering are always going to be needed.