ML Engineer

About the Role

Build and support ML / MLOps capabilities within the Global Imaging Platform. Integrate ML models and workflows with IDPs, ARDs, App Catalog, and model registry.

Responsibilities

• Support SageMaker integration as the primary algorithm development environment
• Package algorithms for repeatable execution, versioning, lineage, and auditability
• Implement model metadata, version control, experiment tracking, and reproducible build patterns
• Support in-platform inferencing workflows for SageMaker-hosted or connected models

Qualifications

• Strong hands-on experience with Python and ML engineering libraries
• Experience with AWS SageMaker, model deployment, and endpoint / inference workflows
• Experience with MLOps, model registry, experiment tracking, model versioning, and reproducibility
• Knowledge of CI/CD pipelines for ML model packaging, deployment, and promotion
• Familiarity with GxP-ready workflows and validation evidence generation

Benefits

• Opportunity to work on a global imaging platform with significant impact
• Collaborate with architects, data scientists, and subject matter experts
• Contribute to the advancement of life sciences through cutting-edge ML engineering