R1 Stage D · 160 h (48 T / 112 P) Anchor track
AI Engineer
Applies pre-trained models and existing AI tooling to build and operate production AI features; does not train foundation models.
Tech stack
PythonFastAPILangChainLangGraphLlamaIndexpgvectorQdrantLiteLLMRedisDockerRAGASLangSmithGitHub Actions
Modules
M1 · Production RAG engineering
40 h TOPICS
- Query understanding and rewriting
- Multi-hop and recursive retrieval
- Reranker selection and tuning
- Chunk and index experimentation methodology
- Freshness and invalidation
- Multi-tenant corpus isolation
Lab: Systematic retrieval-tuning study raising recall@k on a fixed corpus with documented method
Course material for this module is in production.
M2 · Agentic feature delivery
40 h TOPICS
- Workflow decomposition
- Tool contract design
- Durable state
- Retries and compensating actions
- Partial failure handling
- Human-in-the-loop checkpoints
- Multi-agent supervision
Lab: Multi-step agent with durable state, approval gate and full recovery behaviour
Course material for this module is in production.
M3 · Reliability, cost and latency engineering
40 h TOPICS
- Model routing and fallback chains
- Provider outage handling
- Semantic and exact caching
- Streaming
- Concurrency and backpressure
- Rate-limit management
- Token budgeting
- Unit economics
- SLOs and error budgets
Lab: Reduce cost per query by ≥50% and p95 latency by ≥30% at held quality
Course material for this module is in production.
M4 · Ship and operate
40 h TOPICS
- Service packaging
- Configuration and secrets
- CI/CD with evaluation gates that block regression
- Canary release and rollback
- Tracing and alerting
- On-call runbooks
- Post-incident review
Lab: Deploy behind an eval-gated pipeline; force a regression and prove the gate blocks it
Course material for this module is in production.
Track project
Enterprise knowledge assistant: RAG over a multi-format corpus with enforced citations, an agentic action with approval gate, layered guardrails, evaluation harness separating retrieval and generation, documented cost and latency budget, deployed via CI/CD with eval gates, tracing and a runbook.
Job-ready exit standard
Ships a RAG/agent system end to end and defends design under cost, latency and safety constraints; 4+ repositories; interview-ready on RAG and agent system design, evaluation methodology and inference economics.