Senior AI/Data Science Platform Engineer

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About the role

Careers that change lives start here. Medtronic is a global leader in healthcare technology with a Mission to alleviate pain, restore health, and extend life. Our 95,000 employees work across more than 150 countries to put patients first — developing innovative medical technologies that improve the lives of 72+ million patients each year. Your unique talents will help shape the future of healthcare while building a career grounded in purpose, growth, and impact. A Day in the Life Medtronic ’s Surgical Operating Unit (SOU)   is creating ambitious new products that bring connectivity, real-time guidance, and decision support to the operating room. To support this mission, we are developing high-performance systems that enable edge computing and connectivity.    The Surgical AI Centre of Excellence works closely with teams across the SOU , to create  a connected ecosystem and leverag e state of the art data science and computer vision to provide the most comprehensive view of surgery. If you’re interested in using cutting-edge technology to improve the standard of care in surgery on a global scale, this could be the place for you.      As a Senior Platform Engineer working in our AI and Data Science Platform team, you will help design, build and  maintain  cloud and on-prem infrastructure that  Software Engineers, Machine Learning Engineers and Data Scientists use to develop new products. You will use your skills to build a platform that empowers our teams to deliver rapidly and at scale.       Responsibilities may include the following and other duties may be assigned:   • Contribute to the design, development and maintenance of cloud (AWS) and on-prem infrastructure that is used by ML Engineers, Data Scientists and new product development teams.   • Support ML engineers by responding to platform service requests, troubleshooting issues, and improving the reliability and usability of the MLOps platform.   • Build productive working relationships across engineering teams to align stakeholders and move shared technical work forward . • Explain technical ideas, risks, and trade-offs clearly in pull requests, design documents, incident reviews, and meetings.     Required Knowledge and Experience:    • Strong practical Kubernetes experience, including diagnosing and operating Pods, ReplicaSets, StatefulSets, DaemonSets, Services, PVs/PVCs, and Helm deployments. • GitOps-oriented mindset with experience in declarative, version-controlled delivery, PR workflows, peer review, and approval gates. • Cloud infrastructure experience, with AWS strongly preferred. • Strong understanding of cloud primitives including IAM, networking, compute, storage, and observability. • Practical Terraform and/or OpenTofu experience, with the ability to build maintainable, reviewable infrastructure changes. • Hands-on Docker experience, including building, debugging, and maintaining secure, efficient images. • Experience operating production systems, including incident diagnosis, safe change management, and preventing recurrence. • Ability to assess complex platform needs, define practical solutions, and deliver with limited supervision. • Proven ability to improve developer experience, reliability, security, delivery speed, or operational efficiency. • Collaborative team player who communicates openly, gives and receives constructive feedback, and supports colleagues as priorities change. • Willingness to commute to London 2 days per week. Exposure, collaboration with or direct experience in a Kanban and/or Scrum role is preferred. Experience and/or knowledge of Machine Learning is considered a plus.   Physical Job Requirements The above statements are intended to describe the general nature and level of work being performed by employ

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