Location: UK (occasional office travel) | Employment Type: Full-time, Permanent | Reports to: Engineering Manager
We're looking for a Senior Infrastructure Engineer in our Platform team to drive the architecture, design, and implementation of our next-generation AI-native cloud platform. You will implement scalable, resilient infrastructure and the self-service tooling that sits on top of it, working within an agreed architectural direction and contributing to how that direction evolves. You will combine strong hands-on engineering with clear communication, enabling teams to run HPC and AI/ML workloads reliably at scale.
Responsibilities
Infrastructure Design & Virtualisation
•Implement and operate virtualisation solutions optimised for AI-native workloads, including hypervisor tuning for HPC environments
•Build platform capabilities that scale with customer demand for compute, storage and networking
Bare-Metal and Operating System Management
•Deliver provisioning, orchestration and lifecycle management of bare-metal hardware
•Maintain strong Unix/Linux expertise, producing secure and performant configurations at scale
Networking and High-Performance Storage
•Implement high-performance, cloud-native storage and networking for demanding workloads
•Apply solid working knowledge of networking protocols (TCP, UDP, DNS, encryption) and software-defined networking
Kubernetes and Cloud-Native Platforms
•Deploy and manage Kubernetes clusters across hybrid and multi-cloud environments, leveraging CNIs and service meshes
•Build and maintain CI/CD pipelines that automate infrastructure delivery
Security and Audit
•Kubernetes security hardening (RBAC, PSA/PSP successors, network policies, admission controllers - OPA/Gatekeeper, Kyverno)
•Secrets management and rotation across clusters (Vault/OpenBao, sealed-secrets, external-secrets patterns)
•Supply chain security — image scanning, signed artifacts (cosign/sigstore), provenance attestation
•Apply least-privilege IAM patterns across bare-metal, cluster and cloud control planes
Observability and Automation
•Build and maintain observability pipelines covering logs, metrics and tracing
•Create automation frameworks to reduce operational toil, streamline deployments, and enhance scalability using tools like Terraform, Ansible, Go, and Python
Architecture & Solution Design
•Analyse emerging technologies and assess their potential fit within our platform, considering scalability, security, and performance
•Produce low-level designs and implementation plans for platform components, escalating cross-cutting architectural decisions appropriately
•Collaborate with cross-functional teams to ensure successful implementation of infrastructure solutions
•Participate in design reviews, offering constructive technical challenge
•Write clear documentation and runbooks for the systems you build
Collaboration and Leadership
•Foster a "how do we achieve this?" mindset, championing a positive, solution-oriented approach to challenges
•Mentor less experienced engineers through pairing, code review and design feedback
•Work with engineers across teams to deliver infrastructure work that lands end to end
Requirements
•Hands-on experience delivering infrastructure at scale in cloud-native, HPC or AI-native environments
•Demonstrated success in evaluating and adopting new toolsets and technologies
•Extensive expertise in large-scale global infrastructure deployments, particularly in AI-native, HPC, and cloud-native environments
•Strong Kubernetes fundamentals, including container networking (CNI) and service mesh technologies
•Deep understanding of virtualisation and hypervisor technologies for high-performance workloads
•Strong proficiency in networking protocols (TCP, UDP, DNS, BGP) and SDN solutions
•Skilled in Infrastructure-as-Code (IaC) tools like Terraform, Ansible, and cloud orchestration frameworks
•Hands-on coding experience in Go, Python, or similar languages for automation
•Solid grasp of observability tools (Prometheus, Grafana) and distributed tracing systems
•Strong communication and collaboration skills, with the ability to convey complex technical concepts to cross-functional teams
•Demonstrated ability to create clear and concise documentation for infrastructure designs, processes and systems
•Experience with AI/ML workloads, GPU-accelerated systems, or HPC infrastructures, including GPU partitioning with MIG and the NVIDIA GPU Operator
•Experience with RDMA fabrics such as InfiniBand or RoCEv2, and GPUDirect data paths
•High-throughput distributed storage at scale, such as Ceph or a parallel filesystem
•Familiarity with hybrid cloud and edge computing principles
•Experience leading architecture initiatives in agile environments, working iteratively to deliver high-quality results
What's on offer
•Work on a rapidly scaling AI infrastructure business at the forefront of the industry
•Get broad exposure to a cutting-edge physical & software infrastructure stack
•Be part of a team where you're genuinely valued and your work directly enables business decisions
•Develop your career in a Brookfield-backed company with resources, mentorship, and genuine growth opportunities
