Senior AI / Platform Engineer
What you get when AI engineering, data engineering, and cloud/DevOps stack together.
This is a blend, not a single cycle. Not a single cycle. This Advanced Pathway names the senior profile that emerges once you've stacked AI engineering with data engineering and/or cloud/DevOps — someone who can own a platform, not just a feature.
Who it's for
Engineers who have completed AI Engineering plus at least one of Data Engineering or Cloud/DevOps.
Built from these rungs
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.
Cloud / DevOps
The infrastructure rung that makes everything above it deployable.
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.'
What proof looks like
At this level, proof means a coherent portfolio across rungs, not one isolated project.
- A portfolio spanning at least two role rungs with deployed, linked artifacts
- Evidence of owning a system end to end: data in, model/serving in the middle, deployed and monitored out
- A case study tying the rungs together into one coherent platform story