Core AI Engineer

G-Research; Greater London; On-site; GBP 120,000 - 180,000; Full time

Benefits offered by this job

Lunch provided (via Just Eat for Biz)

Dedicated barista bar

30 days annual leave

9% company pension contributions

Comprehensive healthcare and life assu

Job summary

G-Research in London is seeking an Engineer for the Core AI team to design, build and operate the foundational AI platform. You will work across on-prem model inference, controller servers, and secure sandboxing while enabling high-quality research across the firm.

You will influence platform reliability, developer experience, and scalable infrastructure across Kubernetes, with emphasis on secure access, observability and open-source adoption, collaborating closely with Applied AI teams.

Qualifications

• Strong expertise in C# and Python, building distributed systems.
• Deep Kubernetes expertise, multi-tenant clusters.
• Experience with Docker, Terraform and CI/CD in regulated environments.
• Strong understanding of distributed systems, including networking, storage, security and performance.
• Experience with model serving and inference infrastructure.
• Experience with MCP or similar platform services.
• Experience in quantitative finance or low-latency systems.
• Clear communication skills and technical documentation ability.
• Experience with AWS in hybrid environments.
• Familiarity with observability tooling such as Prometheus, Grafana or OpenTelemetry.
• Contributions to open-source projects in relevant domains.

Responsibilities

• 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.
• Evaluating and adopting open-source technologies and applying emerging best practices to improve the platform.

Skills

C#

Python

Kubernetes

Distributed systems

Docker

Terraform

CI/CD

Model serving

Open-source

AWS

Observability

Tools

Prometheus

Grafana

OpenTelemetry

CI/CD pipelines

Job description

We tackle the most complex problems in quantitative finance, by bringing scientific clarity to financial complexity.

From our London HQ, we unite world‑class researchers and engineers in an environment that values deep exploration and methodical execution - because the best ideas take time to evolve. Together we’re building a world‑class platform to amplify our teams’ most powerful ideas.

As part of our engineering team, you’ll shape the platforms and tools that drive high‑impact research - designing systems that scale, accelerate discovery and support innovation across the firm.

Take the next step in your career.

The role

The Core AI team is a centralised infrastructure team within the AI Engineering department. We build, operate and scale 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.

We provide the foundations that enable teams across the firm to innovate and deliver with confidence, working closely with our 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
• Developer AI experience – improve developer experience through seamless integrations and user‑facing tools.

Key responsibilities of the role 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
• Evaluating and adopting open‑source technologies and applying emerging best practices to improve the platform

Who are we looking for?

We value pragmatic engineers who combine deep infrastructure expertise with strong systems thinking and clear communication. You should enjoy building reliable, secure platforms at scale - the kind of foundations that hundreds of engineers and quants depend on daily without needing to think about.

The ideal candidate will have the following skills and experience:

• 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
• Clear communication skills, with ability to explain complex concepts and produce high‑quality technical documentation
• Experience with MCP or similar platform services
• Familiarity with sandboxing and workload isolation technologies
• Experience in quantitative finance or low‑latency systems
• AWS experience particularly in hybrid environments
• Experience with observability tooling such as Prometheus, Grafana or OpenTelemetry
• Contributions to open‑source projects in relevant domains
• Highly competitive compensation plus annual discretionary bonus
• Lunch provided (via Just Eat for Business) and dedicated barista bar
• 30 days annual leave
• 9% company pension contributions
• Informal dress code and excellent work/life balance
• Comprehensive healthcare and life assurance