ML Ops Engineer

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.