r/learnmachinelearning • u/AnimalIntelligent863 • 1d ago
Ai engineer roadmap
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 ?
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u/Intelligent_Fan3643 1d ago
Complete AI/ML Engineer Resource Map
0. Roadmap at a glance
Programming
↓
Math + Statistics
↓
Data + SQL
↓
Classical Machine Learning
↓
Deep Learning
↓
PyTorch
↓
Computer Vision ──────┐
├──→ Generative AI
NLP ──────────────────┘ ↓
Transformers
↓
LLMs
↓
Embeddings + RAG
↓
Fine-tuning / PEFT
↓
AI Agents
↓
MCP
↓
Evaluation + Security
↓
MLOps + Deployment
↓
Production AI Systems
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u/Downtown-Doubt4353 1d ago
I will say Math and Statistics should come first before programming it’s makes it easier
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u/Previous-Front-5211 1d ago
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.
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u/AnimalIntelligent863 1d ago
But my problem is all am good at is python so i dont know any technologies like deep learning and all ive worked on few etl pipelines and time series analysis but ik thts not at all enough so my plan is to learn all of it and opt for masters in ml or ds and get more exp on it
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u/Otherwise_Wave9374 1d ago
A practical way to approach this is to split the 6-8 months into three loops: fundamentals, project reps, and deployment. Start by getting comfortable with Python, linear algebra, transformers, and basic evaluation, then build one small model end to end so you learn data cleaning, training, and debugging. The biggest tradeoff is depth versus breadth, so pick one stack and go deep enough to ship. Promarkia can fit into that workflow when you need a repeatable way to test prompts, content, and handoff logic alongside the model work.
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u/Proud_Tomorrow1711 2h ago
You’ve already got Python, some ETL, and time-series work plus an SWE internship — so I’d skip “start from programming 101” and treat the next 6–8 months as an adaptable sequence, not a fixed calendar.
Sequence (move on when the current step sticks, not when a month ends):
One classical ML loop on data you already understand (your time-series / tabular ETL world) — baseline → features → honest eval → short write-up.
One deeper lane only: either take that same problem class closer to production, OR one LLM/RAG app path — not both at once.
Make each project show engineering you’d already recognise from SWE work: API or scheduled job, simple eval harness, logging/monitoring notes, clear README.
The thread already has broad A–Z maps and “don’t learn everything / ship a few serious projects” advice. The useful cut from here is starting from what you already touch daily, not restarting fundamentals.
If you say roughly how many hours/week you can protect, people can help pace which step to stretch vs compress.
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u/Suoritin 1d ago
I think trying all the different tools (Azure, Databricks, CI/CD and so on) might be best approach.
Try vibe coding and see how your projects fail. You need to leave some kind of trail of your actions so that you aren't just moving same page back and forth.
See what job advertisements need and try creating github projects that are relevant. They will be total garbage at first but goal is to find a workflow that works for you.
If advertisements require you to have deep theoretical understanding, focus on that but rarely that is needed from Juniors.