Career guide
AI Engineer
AI engineering is the work of turning models into useful product behaviour: retrieval, evaluation, workflows, reliability, and deployment.
Capabilities that matter
- Build with LLM APIs and model-serving tools
- Use embeddings, vector search, and retrieval-augmented generation
- Evaluate model output for quality, safety, and cost
- Ship AI features inside existing software systems
Useful prerequisites
- Comfortable programming in Python or TypeScript
- Basic APIs, databases, and software deployment
- Enough probability/statistics to reason about uncertainty
Possible learning sequence
- Prompting and model APIs
- Embeddings and semantic search
- RAG systems and context design
- Evaluation, observability, and failure modes
- Applied project with a real user workflow
Readiness evidence
- A working RAG or agentic workflow
- Evaluation set with pass/fail criteria
- Cost and latency notes for realistic usage
Common mistakes
- Learning model theory before building anything
- Skipping evaluation until after launch
- Treating demos as production systems
Next step
See the learning route for this career.
The career guide helps you decide what matters. The roadmap turns that direction into a practical sequence of foundations, practice, projects, and readiness evidence.
Turn this into today's learning plan.
Roe turns an outcome into the next useful lesson, then checks and reviews what sticks.
