The MLOps Engineer is the engineer who builds and runs the platform that lets Data Scientists and ML Engineers ship their models continuously: training pipelines, model registry, deployment, monitoring, compute resource management.
The profile sits halfway between an SRE (reliability, monitoring, infrastructure as code, Kubernetes) and a data engineer (pipeline orchestration, large volumes, feature stores). They are responsible for the availability and performance of ML services in production.
They mostly work in organizations with several models in production or heavy compute needs (GPUs), where each team can no longer hack together its own pipeline.
Why it matters when hiring
This role is often confused with the ML Engineer: the ML Engineer ships models, the MLOps Engineer builds the tooling used to ship all of them. In a small team, a strong ML Engineer is enough. On a resume, look for a platform built for other users, with Kubernetes, infrastructure as code and production model monitoring.