Evolved from basic deployment, 2026 'LLMOps' requires orchestrating multi-agent systems, implementing strict AI guardrails, and utilizing specialized observabil
MLOps/LLMOps skill operationalizes AI: deployment pipelines, model and prompt registries, eval-gated releases, observability for traces and quality, and cost governance across providers. It's the difference between AI features that ship weekly and pilots that rot.
Structural: every shipped AI feature becomes an operational obligation, and engineers who span classic DevOps plus LLM-specific operations are persistently scarce relative to deployment growth.
Directly, it's the standard path: your pipeline, container, and observability skills carry over; add model serving, eval gating, and token-cost management to complete the profile.
Eval-gated delivery: wiring quality measurement into CI so AI changes ship safely. It's rare, visible, and what separates mature AI teams from hopeful ones.