Machine Learning Operations Engineer

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  • Botley, Oxfordshire
  • Permanent
  • Full-time
  • 1 month ago
  • Apply easily
ML Ops Engineer – Motion Capture Technology | Hybrid (Oxford, UK)An exciting opportunity has arisen for an ML Ops Engineer to join a world-leading technology company specialising in high-performance motion capture solutions for the entertainment, engineering, and life sciences industries. Their products are widely used in feature films, gaming, commercials, and cutting-edge research in biomechanics, robotics, and beyond.You’ll be part of a collaborative R&D team that’s pushing the boundaries of motion capture technology, working in a company with a strong track record of innovation and global impact.The Role:You will join the ML Operations team, supporting the development of next-generation motion capture products. The role involves provisioning and maintaining a modern ML Ops stack, which includes data acquisition pipelines, data management systems, and ML model training infrastructure. This stack combines self-managed on-premises systems with cloud-based AWS resources.As an ML Ops Engineer, you’ll have the opportunity to influence technical direction, propose new solutions, and potentially lead projects within the team.The company offers a hybrid working model, with a head office in a major academic city. There is no on-call expectation outside of core office hours.Key Responsibilities:Manage and maintain on-premise Kubernetes clustersImplement and maintain ML Ops pipelines using Kubeflow and similar tools (e.g., MLflow)Develop scripts and tooling in Python; manage Linux system configurationsLeverage AWS infrastructure (Cognito, S3, EC2, Lambda, etc.)Integrate ML toolkits (e.g., PyTorch, Lightning) into ML Ops workflowsDesign and deploy robust ML Ops solutions across various technologiesContribute to the technical strategy and suggest improvements to the ML Ops stackRequired Skills and Experience:Solid experience managing on-premise Kubernetes clustersStrong knowledge of Kubeflow or similar ML Ops platformsProficiency in Python programming, Linux systems, and scriptingExperience with AWS services (Cognito, S3, EC2, La

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