Infrastructure engineer

AI Infrastructure

Kubernetes, Terraform, and GPUs — the substrate every AI system eventually lands on.

Who this is for

Infrastructure and cloud engineers provisioning the platform underneath the models.

What you should be able to do

Stand up reproducible, cost-bounded infrastructure for AI workloads across clouds.

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.

Landing zone
Network + egress
Identity + secrets
Cluster / compute
GPU scheduling
IaC modules
Cost guardrails
DR + backup

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