AI Engineer Career Roadmap Explorer

Starting point: ML starts with data and a training objective; AI engineering starts with a capable model and a product goal
Core skills: ML leans on statistics, PyTorch, and MLOps; AI engineering leans on API design, prompt engineering, RAG, and evals
Iteration loop: ML iterates in training runs over days; AI engineering iterates in prompts and pipelines over minutes
Overlap: fine-tuning, evaluation, and deployment sit in the shared middle, and many engineers do both

A guided, interactive map of the AI engineering career path, from understanding the role itself to LLM fundamentals and the debate around AGI. Click any topic, read the lesson, and mark it complete to track your progress. Progress is saved in your browser.

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