Senior AI/Data Science Platform Engineer

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 employees assigned to this position, but they are not an exhaustive list of all the required responsibilities and skills of this position.  ‌ Recruitment Fraud Alert   We are aware of phishing scams targeting job