Product and Commercial · Available
AI Engineer
TAI-AIEThe full production stack — model APIs, structured output, retrieval, agents, MCP, evaluation, deployment and cost.
- Effort
- 10 weeks
- Level
- Professional
- Passing
- 70%
Competency blueprint
Assessment weighting is published up front — 20% knowledge, 30% labs, 20% portfolio and 30% capstone.
Knowledge checks
20%A quiz per module, marked immediately.
Practical labs
30%Work done in the tools, submitted as evidence.
Portfolio
20%The assignments that accumulate across the track.
Capstone
30%A realistic brief, graded against a rubric by a human.
Assessment blueprint
Knowledge
20%
Labs
30%
Portfolio
20%
Capstone
30%
70% overall to pass. The capstone must pass independently of every other score. Two retakes per assessment window.
Capstone
Ship an AI feature end to end — ingestion, retrieval, generation, an eval set that catches a real failure mode, and a note on what you'd fix next.
Modules
- LLM and generative AI fundamentals
- Python for AI
- Working with model APIs
- Structured outputs and tool calling
- Embeddings and vector databases
- Retrieval-augmented generation
- Agents
- MCP and external tools
- Evaluation and guardrails
- Deployment, monitoring and cost