AI Engineer Path
From software developer to AI Engineer
Visual Roadmap
10 stages · click any stage to jump
Foundation
Python, APIs, JSON, HTTP, Git, Docker, databases
Python & APIs
Build robust backend services
LLM Fundamentals
Tokens, context windows, model APIs
Prompt Engineering
Structured outputs, function calling
RAG
Retrieval, embeddings, vector search
Tool Calling
Give models the ability to act
Agents
Agent loops, memory, planning
Evaluation
Measure what matters
Production AI
Observability, cost, reliability
Capstone
Ship a production AI system
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
Series in this path
AI Engineering Foundations
FoundationsPython, APIs, HTTP and the engineering craft behind every AI system
Production AI Systems
ExpertObservability, cost, reliability, security and deployment