ML Ops Engineer
RESPONSIBILITIES
• Architect and build a production-grade MLOps platform on Snowflake using Snowpark, Snowflake ML, Model Registry, and Feature Store capabilities.
• Design and operationalize reusable ML pipelines for training, validation, deployment, inference, and monitoring.
• Build MLOps workflows aligned with Bronze, Silver, and Gold layers so model training and inference consistently consume trusted medallion data.
• Establish model lifecycle management standards, including versioning, approval workflows, promotion gates, rollback strategy, and model lineage.
• Partner with data scientists to productionize models quickly and safely, transforming experiments into reliable, scalable services.
• Implement model observability for performance, drift, bias, data quality, and service reliability with actionable alerting and SLOs.
• Automate retraining and refresh workflows using Snowflake Tasks, Dynamic Tables, and event-driven orchestration patterns.
• Partner with data engineering to ensure feature pipelines are reliable, reusable, and synchronized with medallion-layer evolution.
• Define and implement CI/CD for ML workflows, including code, data, models, and configuration, along with testing frameworks and release controls.
• Drive MLOps governance across security, compliance, auditability, reproducibility, and responsible AI practices.
• Lead platform maturation from MVP to enterprise scale, including documentation, developer enablement, and operational runbooks.
REQUIRED QUALIFICATIONS
• 5+ years of experience in ML Engineering, MLOps, or related platform engineering roles.
• Strong Python and SQL expertise, with proven experience building production ML pipelines.
• Hands-on experience with Snowflake data and compute patterns; experience with Snowpark and Snowflake-native ML tooling is strongly preferred.
• Demonstrated experience with model deployment, versioning, monitoring, and lifecycle governance in production.
• Experience implementing CI/CD and testing strategies for ML systems.
• Solid understanding of feature engineering pipelines, training-serving consistency, and data quality controls.
• Experience with cloud infrastructure and services, with AWS preferred.
• Strong collaboration skills and the ability to work cross-functionally with data science, data engineering, and business stakeholders.
PREFERRED QUALIFICATIONS
• Experience with Snowflake Model Registry, Snowflake Feature Store, and model observability within Snowflake.
• Experience designing ML systems on medallion or lakehouse-style data architectures.
• Experience with dbt or similar transformation frameworks.
• Familiarity with streaming or near-real-time inference patterns.
• Experience in high-volume operational domains such as logistics, fleet, route optimization, or environmental services.
• Prior experience building greenfield platforms and defining operating standards from the ground up.
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