Every GenAI product that reaches real users needs someone who can make it reliable, observable, and affordable. That is LLMOps, and it is DevOps with a new payload. If you run pipelines and production systems today, this is a natural move.
The mapping
| DevOps | LLMOps |
|---|---|
| CI/CD gates | Eval gates that block quality regressions |
| Observability (logs, traces, metrics) | Tracing prompts, retrieved context, tokens, latency |
| Cost/FinOps | Token cost budgets, caching, model tiering |
| Deployment | Serving models and GenAI services |
| Incident response | Handling hallucinations, prompt injection, drift |
What to learn
- Evals in CI — run an eval set on every prompt/model change; fail the build on regressions.
- Tracing — capture inputs, retrieved context, tokens, and latency per request.
- Cost controls — budgets, semantic caching, and choosing model size deliberately.
- Safety — input validation, output constraints, and prompt-injection defense.
Your first project
Take a simple GenAI app and make it production-grade: add tracing, an eval gate, a cost budget, and a containerized deploy. That single project demonstrates the exact skills a platform team hires for. See production-ready GenAI architecture for the layers.
Next steps
Work through the AI Engineer Roadmap and choose the cloud/DevOps on-ramp on the learn page. In interviews, tell the story of taking an AI demo to a reliable, observable, cost-controlled system — that is a senior signal.