Mlops Engineer

This is your go-to when you need to build production ML infrastructure that actually scales. It covers the full MLOps stack: Kubeflow and Airflow for orchestration, MLflow and Weights & Biases for experiment tracking, plus cloud-specific tooling across AWS SageMaker, Azure ML, and GCP Vertex AI. You also get Kubernetes deployment patterns, feature stores like Feast, and monitoring setups for model drift. The scope is legitimately comprehensive, maybe even overwhelming if you just need to deploy a single model. Best for teams building serious ML platforms where you need proper CI/CD, model registries, and distributed training infrastructure.