MLOps Engineer

Role Overview

Build and operate a greenfield, enterprise-scale MLOps platform on Snowflake. You’ll design the end-to-end ML production ecosystem from MVP to enterprise scale, including standards, operating model, governance, and observability.

What You’ll Do

• Architect and build a production-grade MLOps platform on Snowflake using Snowpark, Snowflake ML, Model Registry, and Feature Store.
• Design and operationalize reusable ML pipelines for training, validation, deployment, inference, and monitoring.
• Implement workflows that align to Bronze/Silver/Gold (medallion layers) so training/inference consume trusted governed data products.
• Define model lifecycle management: versioning, approval workflows, promotion gates, rollback strategy, and model lineage.
• Partner with data scientists to productionize models safely and quickly.
• Implement model observability for performance, drift, bias, data quality, and service reliability (with actionable alerting and SLOs).
• Automate retraining/refresh using Snowflake Tasks, Dynamic Tables, and event-driven orchestration patterns.
• Collaborate with data engineering to keep feature pipelines reliable, reusable, and synchronized as medallion layers evolve.
• Define CI/CD for ML workflows (code, data, models, configuration) including testing frameworks and release controls.
• Drive MLOps governance across security, compliance, auditability, reproducibility, and responsible AI.
• Lead platform maturation with documentation, developer enablement, and operational runbooks.

Qualifications

• 4+ years in ML Engineering, MLOps, or related platform engineering.
• Strong Python and SQL; proven experience building production ML pipelines.
• Hands-on Snowflake data/compute patterns; Snowpark and Snowflake-native ML tooling preferred.
• Experience with model deployment, versioning, monitoring, and lifecycle governance.
• CI/CD and testing strategies for ML systems.
• Solid grasp of feature engineering pipelines, training-serving consistency, and data quality controls.
• Cloud infrastructure experience (AWS preferred).
• Strong cross-functional collaboration with data science, data engineering, and business stakeholders.

Nice to Have

• Snowflake Model Registry and Feature Store plus in-platform observability.
• ML systems designed using medallion/lakehouse-style architectures.
• Experience with dbt (or similar transformation framework).
• Familiarity with streaming / near-real-time inference patterns.
• Experience in high-volume operational domains (e.g., logistics, fleet, route optimization, environmental services).
• Prior experience building greenfield platforms and defining operating standards from scratch.