MLOps jobs

MLOps is the job of making machine learning boring in the best way: reproducible training, versioned models, automated deployment, and monitoring that catches drift before a customer does. The title gets stretched — some MLOps roles are 80% Kubernetes, others are 80% experiment tracking — so read the actual responsibilities twice.

Tooling rotates through MLflow or Weights & Biases, Docker and Kubernetes, Airflow or Dagster, and Terraform for whichever cloud sits underneath. LLM operations added a new wing: prompt versioning, eval pipelines, and token-cost dashboards. Remote friendliness is excellent, since the whole discipline lives in the infrastructure layer anyway.