Teams at Lightning AI

Operate and scale GPU infrastructure platforms across Linux, bare-metal, cloud, and Kubernetes environments. Design reliable platforms, automate provisioning and operational workflows, deploy improvements, manage observability, respond to incidents, and participate in on-call rotations. Collaborate with infrastructure, networking, customer success, and software teams while troubleshooting complex systems and balancing design, risk, cost, and business outcomes.

Support ML engineering teams running production training and inference workloads across Kubernetes, cloud, Linux, and GPU infrastructure. Diagnose distributed PyTorch, CUDA, NCCL, networking, storage, scheduling, and performance issues; analyze observability data; advise customers during incidents; and improve reliability through tooling, automation, documentation, runbooks, and operational processes. The role is hybrid in London, requires at least two office days weekly, and supports EMEA shifts.

Design and build backend systems for AI agent orchestration, tool execution, workflow management, memory, and state handling. Develop scalable APIs and reliable infrastructure for distributed AI workflows, improve platform reliability and observability, and partner with product, research, and engineering teams to deliver agent capabilities. Own technical initiatives from architecture through production, maintain software quality through testing and continuous delivery, and mentor engineers on backend and distributed-systems practices.