AI DevOps engineer
Build infrastructure for AI services
Who is an AI DevOps engineer?
AI DevOps engineers scale LLM/ML infrastructure—queues, GPUs, traces, optimizing token-dollar burn without forfeiting SLA expectations.
The role
- Automates inference deployments, autoscaling, and resilient queueing.
- Tracks token latency and GPU cost telemetry continuously.
Fit in the team
- Bridges ML product teams with classical SRE expectations responsibly.
- Secures secrets for external AI providers conscientiously.
- Partners finance before invoices spike from rogue workloads.
Skills that matter early
- Kubernetes GPU pools sharding inference workloads sustainably.
- Unified logs, metrics, and traces for cross-team incidents.
- Model CI/CD with proxy sidecars—fast rollback paths.
- Queues and rate limits handling burst traffic politely.
- Batching and prompt-cache tuning stretching LLM budgets.
- Vault or cloud secret managers keeping keys out of git.
How learning works
Short lessons and hands-on practice at your level—from fundamentals to tasks close to real work.
Getting started
Sign up, explore the interface, and take the skills check—we capture your baseline and starting point.
Personal plan
A path tailored to your chosen track: topics, module order, and practical assignments.
Theory and practice together
After each theory block—tasks at your level, from drills to connected work you can show employers.
Feedback
Automated checks where they fit; otherwise breakdowns of mistakes and hints for what to do next.
Your pace
Study anytime; revisit harder topics until you’re confident.
Close to the job
Focus on workflows and deliverables typical for the role—tasks, collaboration, and clear progress updates.
Ready to start learning?
Register on the platform and get a personalized learning plan for this profession.
Try for free. No card required.