Description
CUBE is hiring an MLOps Engineer to own the end-to-end lifecycle of machine learning models in production, including ML pipelines, model deployment and serving, observability, LLM governance, experiment tracking, and platform reliability. The role reports to the Lead Data Scientist and works with the Data and AI Engineering team, requiring 3–4 years of experience in machine learning, Azure, deployment, and pipelines. The position involves building Azure-native infrastructure, managing containerised endpoints, monitoring data drift, governing LLM providers and token consumption, maintaining prompt and model versioning, and applying CI/CD, infrastructure as code, and automated testing.
