Q: What do I need to learn to become an AI Engineer?

Assuming you already have an engineering background (software, data, QA, or data science), the stack comes in three steps, and the order matters.

Step 1: First principles. How LLMs actually work from code: API calls, system prompts, context and conversation management, tool calling, then RAG — embeddings, chunking, vector databases, the full retrieval pipeline. All in raw Python, no frameworks, so you understand every moving part.

Step 2: Advanced AI architectures. Agentic systems, in this order: first the agentic loop, so you understand how an agent actually works. Then frameworks: the OpenAI Agents SDK, LangGraph, and LangChain. Then multi-agent orchestration, handoffs, and MCP.

Step 3: Production. The step that turns skills into a career: deploying to the cloud (AWS), evals, monitoring, CI/CD, security, scaling, and cost control. This is what separates engineers who build demos from engineers who get hired.

Each step builds on the one before it. Most self-taught engineers get stuck because they learn these out of order — frameworks before fundamentals, agents before APIs.

For the full topic-by-topic breakdown, see the curriculum page.