AI Engineer Path
From software developer to AI Engineer
Visual Roadmap
10 stages · click any stage to jump
Foundation
Data, databases, containers and configur…
Python & APIs
Build robust backend services that call,…
LLM Fundamentals
Tokens, context windows, sampling, prici…
Prompt Engineering
Instructions, examples, structured outpu…
RAG
Ingestion, chunking, embeddings, retriev…
Tool Calling
Let models call your functions safely.
Agents
Agent loops, memory, planning and knowin…
Evaluation
Measure quality before and after every c…
Production AI
Observability, cost, reliability, securi…
Capstone
Ship a production AI system end to end, …
Overview
The complete journey from software engineering foundations through production AI systems. Master Python, APIs, LLM fundamentals, prompt engineering, RAG, tool calling, agents, evaluation and production deployment.
Learning outcomes
- Build and deploy production LLM applications
- Implement RAG systems with retrieval, reranking and evaluation
- Design and ship AI agents with tool calling
- Apply production patterns: observability, caching, fallbacks, guardrails
- Diagnose and debug failing AI systems
Stages
Data, databases, containers and configuration: the plumbing every AI system sits on.
AI Engineering FoundationsBuild robust backend services that call, and are called by, other systems.
AI Engineering FoundationsTokens, context windows, sampling, pricing and choosing between hosted and open models.
Instructions, examples, structured outputs and streaming.
Ingestion, chunking, embeddings, retrieval and reranking, with citations.
Let models call your functions safely.
Agent loops, memory, planning and knowing when to stop.
Measure quality before and after every change.
Observability, cost, reliability, security and context engineering.
Production AI SystemsShip a production AI system end to end, and prove it works.