MLOps Engineer

The MLOps Engineer owns the end-to-end ML lifecycle platform, enabling ML Scientists to launch, monitor, and iterate on training runs without managing infrastructure directly. This role involves designing training-as-a-service infrastructure, building CI/CD for models, and managing experiment tracking and data versioning at scale. The position requires partnering with the Science team to translate workflow pain points into platform abstractions and drive cost governance as training costs scale.