ML Ops Engineer

Responsibilities:

• Monitor model quality in production: prediction distributions, feature drift, and model and data freshness.
• Own dashboards, alerts and triage (Grafana, Datadog, Prometheus), with a runbook for every alert.
• Lead model incident triage across infrastructure, model and data, and drive RCAs to closure.
• Work on the Data Quality Monitoring framework and keep training and inference pipelines on Databricks / Spark.
• Support platform migrations (Lakehouse / Unity Catalog, runtime upgrades) with validation before and after each cutover.
• Improve CI/CD and code quality for ML jobs, and onboard model teams through documentation and KT sessions.

Requirements:

• Experience: overall, with at least 2-3 years in MLOps, ML/data engineering or SRE for data and ML systems
• Collaboration: Works daily with US- and Europe-based ML engineers, the Model, Service (MSG) and Tech Ops / SRE teams. Needs regular overlap with US Pacific hours

Skills

AWS, MLOps, Python