LLMOps is the set of practices for putting LLM-based applications into production and running them: versioning prompts like code, continuously evaluating answers, tracing model calls, controlling token costs and detecting drift.
It inherits from MLOps but changes focus: you rarely train the model yourself, you orchestrate prompts, tools, document retrieval (RAG) and guardrails around an external model. Typical tools cover evaluation (Promptfoo, Braintrust, LangSmith) and observability (Langfuse, Helicone, Arize).
It is what separates a prototype that impresses in a demo from a reliable service: without automated tests or monitoring, a prompt or model update can degrade the product without anyone noticing.
Why it matters when hiring
LLMOps is rarely a standalone job: it is a skill expected from an AI Engineer or an MLOps Engineer. To check it, ask how the candidate knows a new prompt version is better than the old one. An answer with an evaluation set, metrics and production monitoring is a real signal; "we tested it by hand" is not.