Engineer

AI Engineer

Build the retrieval, prompting, and evaluation machinery that turns a model into a product.

Who this is for

Engineers shipping LLM and RAG features that other people depend on.

What you should be able to do

Ship a retrieval pipeline with evaluation, structured outputs, and a rollback path.

The delivery flow

The order the work actually happens in. Each step is where a decision gets made and written down, not a chapter heading.

Requirement
Data ingestion
Chunking + embedding
Vector store
Retrieval + rerank
Prompt + structured output
Evaluation harness
Deploy + observe

Reference repository structure

A starting layout that keeps agents, infrastructure, and security policy as first-class directories rather than folders someone adds late.

project/
├── apps/            # web, api, admin surfaces
├── agents/          # planner, researcher, reviewer, orchestrator
├── infrastructure/  # terraform, kubernetes, helm, docker
├── security/        # policies, threat models, IAM scopes
├── monitoring/      # dashboards, alerts, SLOs
├── pipelines/       # CI/CD and evaluation gates
├── tests/
└── docs/

Reading path

12 published guides on this track. Every one is a live page on this site — nothing here is a placeholder.

Reference implementations

Citadel’s open-source repositories for this track — Terraform modules, MCP servers, and reference architectures you can read, fork, and run. Apache/MIT licensed; check each repository for its terms.

Primary sources

The standards, framework documents, and vendor references this track is built against. Go here when you need the authoritative wording rather than a summary — in a security review or an audit, the source is what counts.

Other tracks

Talk through this with an architect