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6–9 months

AI Engineer Roadmap

Build production LLM applications, RAG systems and AI agents.

Who this is for: Python developers who want to build products on top of large language models: chat assistants, document Q&A, and agents that take actions. You do not need to train models yourself. You need solid Python, an understanding of APIs, and the habit of measuring whether your system actually works. Plan for 10–12 hours a week over six to nine months.

What the job really involves. Calling an LLM API takes ten lines of code. The hard parts are everything around it: getting the right documents into the prompt (RAG), forcing reliable structured output, keeping cost and latency under control, stopping prompt injection, and building an evaluation set so you can tell whether a change made things better or worse. This roadmap spends most of its time on those parts.

How to use it: pick one real use case and grow it through the stages, for example answering questions about your college's rule book or a small company's HR policies. Start with a plain prompt, add retrieval, add tools, then deploy it with logging. Keep a spreadsheet of 30–50 test questions with expected answers from week one; it becomes your evaluation set.

A note on change: model names, prices and context limits change every few months. Learn the concepts (tokens, embeddings, retrieval, tool calling, evaluation); they carry over from one provider to the next.

Common mistakes: shipping without an evaluation set, sending whole documents instead of relevant chunks, and trusting model output that should have been validated in code.

1

Python & APIs

4 weeks

LLM apps are mostly web services. Get fluent with HTTP, JSON, async calls and FastAPI so you can call model APIs, handle timeouts and retries, and expose your own endpoints cleanly.

  • Python
  • REST APIs
  • Async basics
  • FastAPI

Milestone project: Wrapper API around a public service

Learn APIs on BeeProton →
2

LLM fundamentals

3 weeks

Understand what a model sees: tokens, the context window and how cost scales with both. Write prompts that are specific and testable, and use structured output so your code receives JSON it can validate, not free text.

  • Tokens & context
  • Prompt engineering
  • Structured outputs
  • Cost & latency

Milestone project: Prompt library for a business use case

Learn Large Language Models on BeeProton →
3

RAG systems

5 weeks

Give the model the right facts at the right time. Split documents into sensible chunks, embed and store them, retrieve the most relevant ones for each question, and measure whether retrieval actually found the answer.

  • Embeddings
  • Vector databases
  • Chunking strategies
  • Evaluation

Milestone project: Chat with your PDFs app

Learn Large Language Models on BeeProton →
4

Agents & tools

4 weeks

Let the model call functions such as searching a database, sending an email or creating a ticket, while your code stays in control. Add step limits, confirmation for risky actions and guardrails on every tool input.

  • Tool calling
  • Multi-step agents
  • Memory
  • Guardrails

Milestone project: Email triage agent

Learn Artificial Intelligence on BeeProton →
5

Production

4 weeks

Log every prompt, response and tool call; defend against prompt injection from user input and retrieved documents; collect user feedback; and re-run your evaluation set before every change goes live.

  • Observability
  • Security & prompt injection
  • Deployment
  • User feedback loops

Milestone project: Deploy an AI assistant with Django

Learn Django on BeeProton →