MLOps & ML InfrastructureEngineering Jobs
Deploy and manage machine learning systems in production. Work on ML pipelines, model serving, monitoring, and infrastructure at scale. Docker, Kubernetes, MLflow, and cloud platform experience valued. $140k-$270k+ salaries.
MLOps engineers bridge the gap between ML research and production systems. As companies move from experiments to large-scale model deployments, the demand for engineers who can build reliable, scalable ML infrastructure continues to surge. MLOps combines software engineering, DevOps, and machine learning knowledge.
Key responsibilities include building and maintaining ML training pipelines, model serving infrastructure, monitoring systems for model drift and data quality, and GPU cluster management. Popular tools include Kubernetes, MLflow, Kubeflow, Weights & Biases, and cloud ML platforms like AWS SageMaker and GCP Vertex AI.
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Frequently Asked Questions
What's the difference between MLOps and DevOps?
MLOps extends DevOps principles to machine learning systems. While DevOps focuses on application deployment and infrastructure, MLOps adds challenges specific to ML: data versioning, model training pipelines, experiment tracking, model registry management, and monitoring for model drift and data quality degradation. MLOps engineers need both infrastructure skills and ML domain knowledge.
What tools do MLOps engineers use?
Core MLOps tools include Docker and Kubernetes for containerization, MLflow or Weights & Biases for experiment tracking, Kubeflow or Apache Airflow for pipeline orchestration, and cloud platforms like AWS SageMaker or GCP Vertex AI. DVC for data versioning, Prometheus/Grafana for monitoring, and Terraform for infrastructure-as-code are also commonly used.