Description

You will own the end-to-end ML lifecycle management for production systems.

Responsibilities

• Manage the ML lifecycle using Azure ML, Data Bricks, and MLflow, covering experiment tracking, model versioning, and promotion across dev/test/prod.
• Design and implement MLOps CI/CD pipelines using Azure DevOps or GitLab, incorporating validation gates (unit/integration/offline-online checks) and deployment strategies like canary/blue-green.
• Monitor production model serving and data pipelines, implementing autoscaling and API gateway integration.
• Establish model observability, including drift detection using tools like Splunk, Azure Monitor, or Dynatrace.

Required Skills

• 10+ years of professional experience in ML/DevOps engineering.
• Expertise with Azure ML, Data Bricks, and MLflow for MLOps tooling.
• Strong proficiency in containerization using Docker and orchestration with Kubernetes (AKS).
• Hands-on experience building and managing CI/CD pipelines with Azure DevOps or GitLab.
• Experience monitoring systems using Splunk, Azure Monitor, or Dynatrace.
• Familiarity with CI/CD concepts and implementing deployment patterns like canary/blue-green.

Key Skills

Education

Any Gradute