Description

Key Responsibilities:

• Architect and build a production-grade MLOps platform on Snowflake, leveraging Snowpark, Snowflake ML, Model Registry, and Feature Store
• Design and operationalize reusable pipelines for training, validation, deployment, inference, and monitoring
• Align ML workflows with Bronze, Silver, and Gold medallion layers to ensure consistent use of trusted data
• Establish model lifecycle management standards, including versioning, approvals, promotion gates, and rollback strategies
• Partner with data scientists to productionize models into scalable, reliable services
• Implement model observability for performance, drift, bias, and data quality, with alerting and SLOs
• Automate retraining and refresh processes using Snowflake Tasks, Dynamic Tables, and event-driven orchestration
• Collaborate with data engineering teams to ensure reliable and reusable feature pipelines
• Define and implement CI/CD pipelines for ML systems, including testing frameworks and release controls
• Drive governance across security, compliance, auditability, reproducibility, and responsible AI practices
• Lead platform maturation, including documentation, developer enablement, and operational runbooks

Requirements

• 5+ years of experience in ML Engineering, MLOps, or platform engineering
• Strong Python and SQL skills, with experience building production ML pipelines
• Hands-on experience with Snowflake data platforms (Snowpark and Snowflake ML strongly preferred)
• Experience with model deployment, versioning, monitoring, and lifecycle governance
• Experience implementing CI/CD and testing strategies for ML systems
• Strong understanding of feature engineering, training-serving consistency, and data quality controls
• Experience working with cloud platforms (AWS preferred)
• Proven ability to collaborate across data science, data engineering, and business teams

Preferred Qualifications

• Experience with Snowflake Model Registry and Feature Store
• Background in medallion/lakehouse data architectures
• Experience with dbt or similar transformation tools
• Familiarity with streaming or near real-time ML inference
• Experience in high-volume operational environments (e.g., logistics, fleet, routing)

Key Skills

Education

Bachelor's degree