MLOps Engineer

Purpose

Provides expertise in Machine Learning Operations, bridging data science and DevOps practices. Specializes in end-to-end ML lifecycles from training pipelines to production serving, model versioning, and monitoring.

When to Use

• Building ML training and serving pipelines
• Implementing model versioning and registry
• Setting up feature stores
• Deploying models to production
• Monitoring model performance and drift
• Automating ML workflows (CI/CD for ML)
• Implementing A/B testing for models
• Managing experiment tracking