At a Glance
- Tasks: Design and build AI infrastructure that powers innovative financial solutions.
- Company: Join G-Research, a leader in finance and technology innovation.
- Benefits: Competitive pay, 30 days leave, healthcare, and monthly events.
- Other info: Inclusive culture with excellent career growth and work/life balance.
- Why this job: Shape the future of AI in finance while working with top talent.
- Qualifications: Strong skills in C#, Python, and Kubernetes; experience in distributed systems.
The predicted salary is between 60000 - 80000 £ per year.
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.
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
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.
Essential 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
Desirable skills and experience:
- 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
Why join us?
- 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
- Cycle‑to‑work scheme
- Monthly company events
G-Research is committed to cultivating and preserving an inclusive work environment. We are an ideas‑driven business and we place great value on diversity of experience and opinions. We want to ensure that applicants receive a recruitment experience that enables them to perform at their best.
If you have a disability or a special need that requires accommodation please let us know in the relevant section.
At G-Research, we are passionate about the intersection of finance, technology, and the future. We offer a dynamic, flexible and highly stimulating culture where world‑beating ideas are cultivated and rewarded. We are proud to employ some of the best people in their field and to nurture their talent in our collaborative working environment.
Software Engineer - AI in London employer: Barlowe LLP
At G-Research, we pride ourselves on being an exceptional employer, offering a dynamic and inclusive work environment in the heart of London. Our commitment to employee growth is reflected in our comprehensive benefits package, including competitive compensation, generous annual leave, and opportunities for professional development. Join us to collaborate with world-class researchers and engineers, where your innovative ideas will be valued and nurtured as we tackle complex challenges in quantitative finance together.
StudySmarter Expert Advice🤫
We think this is how you could land Software Engineer - AI in London
✨Tip Number 1
Network like a pro! Reach out to current employees on LinkedIn or at industry events. A friendly chat can give you insider info and maybe even a referral, which is always a bonus!
✨Tip Number 2
Prepare for those technical interviews! Brush up on your C# and Python skills, and be ready to discuss distributed systems and Kubernetes. Practice coding challenges to keep your skills sharp.
✨Tip Number 3
Show off your projects! If you've worked on any relevant open-source projects or personal initiatives, make sure to highlight them. It’s a great way to demonstrate your hands-on experience and passion for the field.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining our team.
We think you need these skills to ace Software Engineer - AI in London
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter to highlight your experience with C#, Python, and distributed systems. We want to see how your skills align with the role, so don’t hold back on showcasing relevant projects!
Showcase Your Communication Skills:Since clear communication is key for us, include examples of how you've explained complex concepts in previous roles. Whether it’s through documentation or presentations, let us know how you make tech understandable!
Highlight Your Experience with Infrastructure:We’re looking for engineers who love building reliable platforms. Share your experiences with Kubernetes, Docker, and CI/CD processes, especially in regulated environments. This will help us see your fit for the Core AI team.
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. We can’t wait to see what you bring to the table!
How to prepare for a job interview at Barlowe LLP
✨Know Your Tech Stack
Make sure you’re well-versed in C# and Python, as these are crucial for the role. Brush up on your knowledge of distributed systems and Kubernetes, as you'll likely be asked to discuss your experience with these technologies during the interview.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in building or operating infrastructure. Use the STAR method (Situation, Task, Action, Result) to structure your answers, highlighting how you approached complex problems and what the outcomes were.
✨Communicate Clearly
Since clear communication is key, practice explaining complex technical concepts in simple terms. You might be asked to explain your thought process or decisions, so being able to articulate your ideas clearly will set you apart.
✨Research the Company Culture
Familiarise yourself with G-Research’s values and work environment. They value diversity and collaboration, so think about how your experiences align with their culture and be ready to share examples that demonstrate your fit within their team.
