AI/Data Science Platform Engineer

experience, including diagnosing and operating Pods, ReplicaSets, StatefulSets, DaemonSets, Services, PVs/PVCs, and Helm deploymentsA GitOps-oriented mindset and experience with declarative, version-controlled delivery, PR workflows, peer review, and approval gatesCloud infrastructure experienceStrong understanding of cloud primitives, including IAM, networking, compute, storage, and observabilityPractical Terraform and/or OpenTofu experience, with the ability to build maintainable, reviewable infrastructure changesHands-on Docker experience, including building, debugging, and maintaining secure, efficient imagesExperience operating production systems, including incident diagnosis, safe change management, and preventing recurrenceAbility to assess complex platform needs, define practical solutions, and deliver with limited supervisionProven ability to improve developer experience, reliability, security, delivery speed, or operational efficiencyCollaborative team player who, communicates

openly, gives and receives constructive feedback, and supports colleagues as priorities changeWillingness to commute to London 2 days per weekБудет плюсом: Exposure to, collaboration with, or direct experience in a Kanban and/or Scrum role; experience and/or knowledge of Machine Learning; AWS experienceУсловияThe position is eligible for the short-term Medtronic Incentive Plan (MIP) Flexible benefits package and competitive compensation plans #J-18808-Ljbffr

ОписаниеMedtronic is a global healthcare technology company that develops medical technologies to alleviate pain, restore health, and extend life. Its Surgical Operating Unit creates products that bring connectivity, real-time guidance, and decision support to the operating room. The Surgical AI Centre of Excellence uses data science and computer vision to provide a comprehensive view of surgery.ЗадачиContribute to the design, development, and maintenance of cloud and on-prem infrastructure used by ML Engineers, Data Scientists, and product development teamsSupport ML engineers by responding to platform service requests, troubleshooting issues, and improving the reliability and usability of the MLOps platformBuild productive working relationships across engineering teams to align stakeholders and advance shared technical workExplain technical ideas, risks, and trade-offs clearly in pull requests, design documents, incident reviews, and meetingsТребованияStrong practical Kubernetes