Give AI builders a safe runway.
Create the delivery platform, guardrails, observability, and reliability practices that let AI-assisted teams move safely.
- In planning
- No launch date is promised
- Remote-first
- Proposed delivery format
- Practical proof
- Capstone-led curriculum design
- Interest only
- Not an internship application
Why this belongs on the roadmap.
Google Cloud's 2025 DORA research describes a high-quality internal platform as a key enabler for scaling AI-assisted development. The planned track would connect DevOps fundamentals to the guardrails and feedback loops AI changes make more urgent.
Research sources support the direction, not a launch date or outcome. This page does not promise a job, salary, client, income, or final curriculum.
The work this curriculum would cover.
Each planned module is designed to produce evidence, moving from foundations to work a real operator can inspect.
- 01Run containerized services locally and explain their operating boundaries
- 02Create reproducible infrastructure with OpenTofu
- 03Use GitOps and CI/CD with reviewable promotion gates
- 04Build observability across logs, metrics, traces, cost, and model behavior
- 05Design developer guardrails for AI-assisted code and dependencies
- 06Run an incident drill, recovery exercise, and blameless postmortem
The proof this track would demand.
A working internal delivery platform for an AI service: infrastructure code, GitOps flow, CI gates, dashboards, cost controls, a reliability drill, recovery evidence, and a written postmortem.
The working stack.
Tools can change before launch. The proposed workflow and quality bar are the durable part.
- Docker
- Kubernetes
- OpenTofu
- Argo CD
- GitHub Actions
- Prometheus
- Grafana
- OpenTelemetry
Register interest in this track.
Join the track-specific waitlist. This is separate from the internship intake waitlist and does not submit an application.