Description

Architect and operate a production-grade MLOps platform on Snowflake to productionize ML models.

This role is on-site.

Responsibilities

• Design and operationalize reusable pipelines for training, validation, deployment, inference, and monitoring using Snowpark and Snowflake ML.
• Implement model lifecycle management, including versioning, approval gates, promotion strategies, and rollback mechanisms.
• Build CI/CD pipelines for ML systems with automated testing, release controls, and governance for security and compliance.
• Establish model observability for performance, drift, bias, and data quality, defining alerting and SLOs.
• Automate retraining and refresh processes using Snowflake Tasks, Dynamic Tables, and event-driven orchestration.

Required Skills

• 5+ years of experience in ML Engineering, MLOps, or platform engineering.
• Strong Python and SQL skills with a track record of building production ML pipelines.
• Hands-on experience with Snowflake data platforms, specifically Snowpark and Snowflake ML.
• Experience with model deployment, versioning, monitoring, and lifecycle governance.
• Proficiency in implementing CI/CD and testing strategies for ML systems.
• Deep understanding of feature engineering, training-serving consistency, and data quality controls.
• Experience working with cloud platforms, with AWS preferred.

Preferred Skills

• Experience with Snowflake Model Registry and Feature Store.
• Background in medallion/lakehouse data architectures and tools like dbt.
• Familiarity with streaming or near real-time ML inference in high-volume environments.

Key Skills

Education

Bachelor's degree