Data & AI Infrastructure Team Leader

Job Description Summary

GE HealthCare is a global leader in medical technology and digital solutions innovation. Our technology helps streamline workflows, automate manual tasks, and assist physicians through clinical decision support.

We are seeking an exceptional Data & AI Infrastructure Team Leader to join our Point of Care and Handheld Ultrasound AI team in Haifa.

In this role, you will lead the development of data pipelines, workflows, and infrastructure, and drive the creation of frameworks and APIs that integrate AI components into our clinical ultrasound applications.

You will deliver modern, scalable engineering solutions that support the growing AI needs of a fast‑moving, regulated medical domain.

Responsibilities

Job Description

• Lead and mentor a team of engineers driving the design and development of data and integration infrastructures supporting ultrasound AI development.
• Design, develop, and maintain data pipelines, data workflows, and scalable data infrastructure for clinical and algorithmic needs.
• Build and operate frameworks and APIs that integrate AI components into ultrasound applications and engineering workflows.
• Own end‑to‑end data quality, metadata, traceability, and compliance required for clinical and regulatory workflows.
• Drive automation, CI/CD practices, testing rigor, and engineering efficiency across data and AI‑related processes.
• Implement and promote the use of GenAI tools to improve development, documentation, and data operations.
• Collaborate closely with AI, Clinical, Verification, and SW teams to support end‑to‑end AI delivery.
  
  

• 5+ years of hands‑on experience in data engineering and/or backend/infrastructure engineering for AI‑driven systems, with strong ownership of complex, scalable solutions.
• Proven leadership experience: guiding engineers, driving execution, defining direction, and delivering end‑to‑end solutions.
• Experience designing and delivering scalable data workflows or infrastructure components in production environments.
• Hands‑on experience integrating AI components into applications through SDKs or APIs (e.g., inference services, model wrappers, AI‑enhanced product features).
• Strong engineering fundamentals: testing (unit/integration/E2E), automation, and CI/CD processes.
• Excellent communication and collaboration skills across multidisciplinary teams (Algo, Clinical, Verification, Product).
• Experience developing services or APIs that connect data and AI components to product software (advantage).
• Experience with cloud platforms, orchestration, or DevOps tools supporting data/AI workflows (advantage).
• Background in regulated industries or medical devices (advantage).
• Experience applying GenAI tools to improve engineering, documentation, or data operations (advantage).