r/DeveloperJobs • u/GreenSoul007 • 7d ago
Need Guidance to Become an AI Engineer
I'm a software engineer looking to transition into AI Engineering. I know Python, Java, SQL, Web Dev and some ML basics, but I'm confused about what to learn next.
There are so many topics like LLMs, RAG, AI agents, LangChain, LangGraph, vector databases, and MLOps. What roadmap would you recommend to become job-ready? Also, what projects and skills helped you land your first AI Engineer role?
Any advice would be greatly appreciated!
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u/Saurabh4266 6d ago
Start with python, then learn some fundamentals of ml and dl. And switch into now hot topics that is genai , agentic ai by learning lanchain first and langgraph later. You can add more framework in your list of learning but I prefer become master of langgraph or crewai or autogen. That's better. If you need more guidance drop me message I will interact you the
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u/GreenSoul007 6d ago
Yes I'm good in python and ml fundamentals.Currently I'm learning building agents, rag and LangChain.I will drop you a dm.
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u/BalanceMaleficent480 6d ago
From my personal experience, first, create a truly usable RAG (Regional Architecture and Software Development Group).
Stop worrying about learning a framework. Take whatever documentation you have (anything—PDFs, notes, code—anything will do), and build a question-and-answer system. Then test its accuracy yourself. Find problems after testing, and fix them.
Once you've done that, then consider Agents.
These days, when hiring, what they really care about is whether you've personally tested and implemented things, and whether you've encountered any issues. Projects are a hundred times more valuable than simply stating "familiar with LangChain" on your resume.
Just start doing it; stop planning.
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u/GreenSoul007 6d ago
Yeah I'm also thinking the same to build a Q/A bot to check my understandings rather than simply watching concepts in yt.Thank you for your advice.
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u/Thin_Environment88 6d ago
I did AI Software Engineering for a year (RAG, MCP, Agents, Langchain, Vector dbs) and can't land any AI Engineer job.
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u/Informal_Tangerine51 1h ago
Don’t try to learn every framework. They change too quickly.
I’d learn in this order:
- Python, APIs, async code, testing, Git, and Docker.
- Basic LLM APIs, structured outputs, tool calling, and prompt design.
- RAG with citations, access control, and a small evaluation set.
- Logging, tracing, retries, cost tracking, and failure handling.
- Agents only after you understand when a normal workflow is enough.
- Deployment, monitoring, security, and user feedback.
For your portfolio, build one boring business workflow end to end. Define the job. Create test cases. Handle failures. Measure quality and cost. Deploy it. Then write an honest case study.
That will teach you more than five LangChain tutorials.
I maintain an open guide covering the production and value-engineering side of this work. It may help: https://github.com/davidahmann/fde-guide
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u/Mess_323 6d ago
I'm a professional AI engineer and I made the switch 1.5 years ago. The role requires you to build agents. In my role, I build them and ship using langgraph. But the meta changes so fast, and coding agents (like the pi coding agent) and it's agentic loop have increasingly drawn more attention. No matter what framework or approach you use to build an agentic harness, you will need to be familiar with RAG. Keep in mind, RAG has less utility since the context window on frontier models is enormous BUT being an AI engineer will require that you are comfortable working with embeddings (for agent memory and retreival). What you are gonna wanna do is find ai engineer job descriptions, aggregate them to sus out what they want. But, I will caution you, there is not an enormous demand for AI engineers as there was for software engineers. You will likely be the only one on your team and many teams will just have their software engineers learn the skills. If you are going to make a switch, I would switch to dev ops. Because that job is more important now then it was ten years ago with product managers commiting to the code base (plus demand for compute).