AI Engineer
A research-backed roadmap from Python foundations to production AI systems — LLM APIs, RAG pipelines, LangGraph agents, fine-tuning, and evaluation in the order that 2026 AI product teams are hiring for.
Python & ML Foundations
2–3 weeksAI engineering is Python-first — async patterns for concurrent API calls, Pydantic for LLM output validation, and enough ML theory to make principled architecture decisions.
LLM APIs & Prompt Engineering
2–3 weeksMaster the interfaces and techniques that turn raw LLM capabilities into reliable, production-grade AI applications — from chat completions to structured outputs.
Embeddings & Vector Search
2–3 weeksThe representation layer of AI — convert text and documents to semantic vectors, choose the right vector store, and build hybrid search that outperforms either approach alone.
RAG Systems
3–4 weeksBuild the retrieval-augmented generation pipelines that power 80%+ of production AI applications — from fundamental architecture to advanced retrieval strategies.
Agentic AI
3–4 weeksBuild AI agents that take multi-step actions using tools — from the fundamental agent loop to production-grade LangGraph workflows with observability and safety.
LLM Evaluation & Observability
2–3 weeksBuild the evaluation and monitoring infrastructure that prevents AI quality regressions — LLM-as-judge, distributed tracing, and production safety guardrails.
Fine-Tuning & Customisation
2–3 weeksKnow when fine-tuning beats prompting, how to build training datasets that actually work, and how to evaluate whether the fine-tuned model is an improvement.
Production, Safety & Portfolio
2–3 weeksDeploy AI systems reliably, document safety measures for regulated industries, and build the portfolio that demonstrates full-stack AI engineering experience.
Ready to start your path?
Python and LLM API fundamentals appear in 95%+ of AI engineer job postings — start with the foundations.