MLOps engineer

An MLOps engineer builds the infrastructure and processes that keep machine-learning models running reliably in production. They automate the pipeline from training to deployment, set up monitoring to detect model drift and failures, manage versioning of models and data, and ensure models can be retrained and updated smoothly at scale.

The role applies DevOps principles to the special challenges of ML — models that decay over time and depend on shifting data. MLOps engineers work wherever ML runs in production, bridging data science and operations.

The job suits systems-minded engineers who want models to work dependably in the real world, not just in experiments.