About this role
G-Research is hiring a Core AI Engineer in London to build and run the platform that powers AI work across the firm. The role covers on-prem model inference and serving, centralised MCP servers, secure sandboxing for autonomous agents, and tools that improve the developer experience. The engineer will own reliability, performance and scale across Kubernetes-based infrastructure, including observability, capacity planning and incident response. Applicants need strong C# and Python skills, deep Kubernetes expertise, and experience with Docker, Terraform, CI/CD and model serving infrastructure.
Your Career Hunter matched this listing against the employer's careers page on Nov 15, 2026.
Job Overview
Job description provided by G-Research.
G-Research is a quantitative finance firm based in London. The Core AI team is a centralised infrastructure team within the AI Engineering department. The team builds, operates and scales the foundational platform that powers AI innovation across G-Research, including on-prem open model inference, model serving, AI developer experience tooling, centralised MCP servers and secure agent sandboxing. The team provides the foundations that enable teams across the firm to innovate and deliver with confidence, working closely with the Applied AI team. As an Engineer in Core AI, you will work across four key areas: infrastructure and serving - design, build and operate on-prem model inference and serving platforms; MCP server infrastructure - build and operate centralised MCP servers that provide secure, governed access to tools and data; security and sandboxing - design and implement infrastructure for safe execution of autonomous AI agents in a regulated environment; and developer AI experience - improve developer experience through seamless integrations and user-facing tools. Key responsibilities include designing and operating model serving infrastructure, including inference pipelines and scheduling systems; building and running centralised MCP servers, ensuring secure, reliable access to enterprise tools and data; owning platform reliability, performance and scalability across Kubernetes-based infrastructure, including observability, capacity planning and incident response; building self-service tooling and APIs to enable teams to provision and consume AI infrastructure independently; integrating platform services with existing technology stacks, ensuring clear interfaces, monitoring and CI/CD; and evaluating and adopting open-source technologies and applying emerging best practices to improve the platform. Essential experience includes strong expertise in C# and Python, building distributed systems and platform-level software; deep Kubernetes expertise, including multi-tenant cluster operations and platform extensions; experience with Docker, Terraform and CI/CD in controlled or regulated environments; strong understanding of distributed systems, including networking, storage, security and performance; experience with model serving and inference infrastructure, including deployment, scaling and optimisation of open models; and clear communication skills. Desirable experience includes MCP or similar platform services, sandboxing and workload isolation technologies, quantitative finance or low-latency systems, AWS in hybrid environments, observability tooling such as Prometheus, Grafana or OpenTelemetry, and contributions to open-source projects. Benefits include highly competitive compensation plus annual discretionary bonus, lunch provided, 30 days' annual leave, 9% company pension contributions, informal dress code, comprehensive healthcare and life assurance, cycle-to-work scheme and monthly company events. Location: London.