AI Infrastructure Engineer (GPU, Distributed Systems, AI Platforms)

Role and responsibilities for the AI Infrastructure Engineer, GPU, Distributed Systems & AI Platforms

• Build, operate and optimise large-scale GPU infrastructure for AI training and inference
• Support multi-node, multi-GPU environments and distributed workloads
• Improve cluster health, fault tolerance and remediation workflows across GPU fleets
• Optimise GPU-to-GPU communication, workload performance and infrastructure utilisation
• Work with high-performance storage systems supporting large datasets and checkpointing
• Build or improve tooling for profiling, monitoring, benchmarking and performance analysis
• Collaborate closely with ML researchers, platform teams and infrastructure engineers to remove bottlenecks and improve training efficiency
• Support capacity planning and deployment for next-generation compute environments

A leading AI business is hiring an AI Infrastructure Engineer who has experience with GPU, Distributed Systems & AI Platforms. Hybrid/Remote options available. Outside IR35. Paying between £800 to £1000 per day.

Experience and skills required for the AI Infrastructure Engineer, GPU, Distributed Systems & AI Platforms

• Strong systems-level engineering experience, ideally in infrastructure, HPC, platform engineering or AI/ML environments
• Hands-on experience operating large-scale compute or GPU-backed infrastructure
• Experience with distributed systems and multi-node environments
• Familiarity with NCCL and GPU-to-GPU communication
• Experience with Kubernetes, containerised platforms and cluster orchestration
• Strong coding ability in Python, Go or C++
• Experience working with high-performance storage across complex environments is highly desirable
• A strong troubleshooting mindset with the ability to understand behaviour at cluster, hardware and network level

Nice to have

• Exposure to InfiniBand, bare-metal provisioning or HPC-style networking
• Experience supporting training or inference environments for large-scale ML models
• Background in AI infrastructure start-ups, hyperscalers or high-performance compute environments
• Experience with profiling / benchmarking tools and performance optimisation at scale