Machine Learning Engineer - Platforms

Role Overview

Machine Learning Engineer (Platforms) within Data Impact & Governance. You will build and scale an enterprise AI/ML platform that enables clinicians, researchers, and data scientists to deliver safe, efficient, and high-impact AI in healthcare.

Responsibilities

• Develop, administer, and maintain the enterprise AI/ML platform (Dataiku, Kubernetes, Azure), ensuring scalability, reliability, and smooth integration with institutional systems.
• Orchestrate ML workflows in Dataiku: training, deployment, and inference pipelines targeting Azure and on-premises Kubernetes clusters.
• Build and maintain MLOps workflows for reproducibility, version control, governance, and end-to-end model lifecycle management.
• Manage and optimize containerized environments using Docker and Kubernetes to support data science workloads.
• Provide platform support to data scientists/ML engineers (troubleshooting environments, pipelines, and dependencies).
• Monitor and optimize platform performance, cost, security, and compliance to align with enterprise and regulatory standards.
• Support healthcare data integration as needed using HL7, FHIR, and/or DICOM.
• Share platform knowledge via documentation, training, and cross-team collaboration; communicate updates, risks, performance, and issue resolutions.

Requirements

• Bachelor’s degree (required).

Nice-to-haves / Additional Details (from posting)

• Experience supporting scalable pipelines for feature engineering, model tracking, and validation in Dataiku.
• Ability to debug and resolve complex platform/pipeline issues.
• Comfort working with healthcare data integration standards (HL7/FHIR/DICOM) when required.