Description

You will own the engineering lifecycle for machine learning services, from research notebooks to production deployment.

This role is on-site.

Responsibilities

• Own and manage end-to-end CI/CD pipelines for ML services across development, testing, and production environments.
• Implement deployment strategies like blue/green and canary releases, including automated rollback.
• Monitor model performance, data drift, and system health, triggering retraining when necessary.
• Develop and maintain production dashboards, alerting systems, and incident response workflows.
• Convert research notebooks into scalable, production-ready services with proper testing and CI integration.

Required Skills

• 5+ years of professional experience in production ML systems and MLOps practices.
• Strong proficiency in Python and SQL.
• Hands-on expertise with MLflow, Snowflake, and Power BI.
• Experience with API development using FastAPI or Flask.
• Solid understanding of OLAP/OLTP data modeling and architecture.
• Proficiency with Git and GitLab for version control.
• Experience integrating ML models with CI/CD pipelines and experiment tracking tools.

Key Skills

Education

Any Gradute