Associate Manager, Production AI/ML Engineering

What you'll do

• Architect and maintain a scalable AWS and Kubernetes ecosystem for model training and real-time serving.
• Design production-grade ML pipelines for ingesting and transforming population-scale clinical and claims data.
• Develop RESTful APIs to reliably expose model inference to downstream teams and applications.
• Operationalize models by managing deployment, versioning, rollbacks, and A/B testing.
• Build MLOps infrastructure including CI/CD pipelines, feature stores, and model registries.
• Own monitoring, alerting, and incident response for all production ML systems.
• Ensure all pipelines and deployments adhere to data governance and de-identification standards.
• Shape the long-term technical roadmap and capacity planning for the AI/ML platform.

What we're looking for

• Bachelor's degree in Computer Science, Software Engineering, Data Science, Biomedical Informatics, or related field.
• Master's degree in a relevant field (preferred).
• 7–9 years of progressive ML, platform, or software engineering experience with an infrastructure focus.
• Proven experience designing and operating ML infrastructure at enterprise scale on AWS.
• Strong Kubernetes expertise including cluster management, workload scheduling, and deploying containerized ML services.
• Proficiency building and maintaining RESTful APIs including design, versioning, and performance tuning.
• Hands-on experience with Postgres schema design and query optimization.
• Expert-level Python and SQL with strong software engineering fundamentals like Git and CI/CD.
• Proficiency with MLOps tooling including MLflow, W&B, Docker, and Airflow or Prefect.
• Informatics knowledge with health or life sciences data (preferred).