MLOps Engineer

MLOps Engineers build and maintain infrastructure for deploying, monitoring, and managing machine learning models in production. They create automated pipelines for model training, versioning, deployment, and performance tracking at scale.

Key Responsibilities: • Design and implement ML deployment pipelines • Build model monitoring and observability systems • Automate model training and retraining workflows • Manage model versioning and experiment tracking • Optimize infrastructure costs and performance • Ensure model reliability and uptime

Required Skills: Kubernetes, Docker, CI/CD, cloud platforms (AWS/GCP/Azure), MLflow/Kubeflow, model serving (TensorFlow Serving, TorchServe), monitoring tools, infrastructure as code, Python, distributed systems, DevOps practices

Career Path: Junior MLOps engineers maintain existing ML infrastructure. Mid-level engineers design deployment systems. Senior MLOps engineers architect company-wide ML platforms. Staff engineers set MLOps standards and build internal tools.

Top Hiring Companies: Netflix, Uber, Airbnb, DoorDash, Spotify, LinkedIn, Meta, Google, Amazon, Databricks, Weights & Biases, Tecton, Feast