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 to process and normalize large-scale clinical and claims data.
• Develop RESTful APIs to expose model inference to downstream applications and teams.
• Operationalize models by managing deployment, versioning, rollback procedures, and A/B testing.
• Build MLOps infrastructure including CI/CD pipelines, feature stores, and model registries.
• Manage monitoring, alerting, and incident response for production machine learning systems.
• Ensure all data pipelines and deployments comply with governance and de-identification standards.
• Develop 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 a related field (Master's preferred).
• 7–9 years of progressive experience in ML, platform, or software engineering with an infrastructure focus.
• Proven experience designing and operating enterprise-scale ML infrastructure on AWS using services like SageMaker, ECS, EC2, S3, and Lambda.
• Strong expertise in Kubernetes for cluster management, workload scheduling, autoscaling, and deploying containerized ML services.
• Proficiency building and maintaining RESTful APIs with experience in design, versioning, and performance tuning.
• Hands-on experience with Postgres including schema design and query optimization.
• Expert-level Python and SQL skills combined with strong software engineering fundamentals like Git, modular design, testing, and CI/CD.
• Proficiency with MLOps tooling such as MLflow, W&B, Docker, and orchestration tools like Airflow or Prefect.