Cloud / DevOps
The infrastructure rung that makes everything above it deployable.
Overview
One 90-day cycle covering cloud fundamentals (AWS/Azure), containers, CI/CD, and observability. This rung is what turns 'it runs on my machine' into 'it runs in production.'
Who it's for
Engineers who want infrastructure/platform roles, or who need deployable systems as a base for AI, data, or inference work.
Prerequisites
What proof looks like
From here on, proof means shipped artifacts and verifiable systems — not screenshots or certificates.
- A CI/CD pipeline you built, not copy-pasted, with passing deploys
- At least one production-style deployment (containerized, monitored)
- Infrastructure-as-code for something you actually run
Where this leads next
AI Engineering
One cycle to a hireable AI engineering role.
Applied GenAI and agentic systems: RAG pipelines, tool-using agents, evaluation, and production hardening. This is a role rung, not the whole ladder — it's one of several tracks you can stack.
Data Engineering
One cycle to a hireable data engineering role.
Pipelines, orchestration, warehousing, and data quality at a level that survives contact with production data. Pairs naturally with AI engineering or cloud/DevOps for senior blends.
Inference Engineering
One cycle to a hireable inference/serving role.
Model serving, latency and throughput optimization, GPU-aware deployment, and inference cost control. This is the rung between 'a model exists' and 'a model serves traffic reliably and cheaply.'