How long does it take to become an AI engineer?

Stage 1 - Python and backend basics (months 1 to 6 from zero)

Learn Python well, then Git, HTTP, JSON and REST APIs with FastAPI or Flask. Learn basic SQL. Build: a small API with a database, a script that calls a public API and handles errors. Skip this stage if you already work as a developer.

Stage 2 - LLM features (about 2 to 3 months)

Use an LLM SDK such as openai. Design prompts, ask for JSON and parse it safely, handle timeouts and rate limits, stream tokens to the user, and cache answers in Redis. Build: a summarization API, a text classifier, a streaming chat endpoint.

Stage 3 - RAG, evaluation and production (about 2 to 3 months)

Create embeddings and store them in a vector database such as Chroma or pgvector. Build RAG over your own documents. Measure quality with an evaluation set and tools such as Ragas or DeepEval. Add guardrails and cost limits. Start applying with 3 to 5 projects that show AI inside a working service.

What makes it faster

Build into real services, not only notebooks. Employers want to see that you can handle the parts around the model: errors, bad output, latency and cost. Keep each project small and write it up clearly - the problem, what you built and how you tested it.

Real AI/ML work to put in your portfolio

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