2 of 2 Cloud Infrastructure Engineer Jobs in the Thames Valley

Cloud Engineer Oracle Fusion & Cloud Infrastructure Location: Oxford, UK (Hybrid working available after initial onboarding) Reporting to: Head of Application Integration Services Effective Date: ASAP About the Organisation (World-Leading Research Institute) Join a revolutionary Global Research and Technology Institute based in Oxford, committed to tackling humanity's most enduring challenges across Health, Climate Change, Food … Security, and AI & Robotics. The Role: Cloud Engineer Oracle Fusion & Cloud Infrastructure This is a critical technical role focused on the design, implementation, and maintenance of our secure, high-availability cloud environments, directly supporting the operation and integration of our Oracle Fusion Cloud (ERP/HCM/SCM) applications. You will be instrumental in … managing the Oracle Cloud Infrastructure (OCI) landscape, driving DevOps automation (IaC/CI/CD) , optimizing performance, and ensuring compliance across all environments. If you are passionate about cloud automation, infrastructure security, and the operational excellence of mission-critical ERP systems, this is the role for you! Key Responsibilities & Focus Areas OCI Infrastructure Management: Design More ❯

Role: ML & Cloud Infrastructure Engineer Salary: £140,000 - £160,000 + Equity Location: London - Hybrid (3-5 days a week) Our client is pioneering a frontier 3D foundation model at the intersection of AI, computer vision, and spatial computing. Their mission is to transform how industries from robotics and AR/VR to gaming and film create … and interact with 3D content. They are seeking an ML & Cloud Infrastructure Engineer to design and maintain the scalable infrastructure that powers cutting-edge machine learning workloads and production systems. This is a chance to work at the forefront of AI innovation in 3D technology, with huge scope to shape the platform and practices in a … fast-moving startup environment. The role will involve: Building and maintaining scalable cloud infrastructure (AWS, GCP, Azure) for ML workloads and APIs Setting up ML nodes for distributed training and local development Managing containerised environments (Docker, Kubernetes, Terraform) Optimising storage for big data pipelines supporting ML workloads Monitoring systems and responding to incidents, ensuring reliability and performance Working More ❯