At a Glance
- Tasks: Shape AI platforms and tools that drive high-impact research in finance.
- Company: Join a leading tech firm in London focused on innovative AI solutions.
- Benefits: Competitive pay, 35 days leave, free lunch, and a relaxed dress code.
- Other info: Inclusive culture with great career growth and monthly company events.
- Why this job: Make a real impact by transforming how teams work with cutting-edge AI technology.
- Qualifications: Strong software engineering skills and experience with modern AI technologies.
The predicted salary is between 70000 - 90000 £ 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.
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 Applied AI team sits at the centre of this effort, building the AI layer that will transform how teams across the firm work, from quant research to engineering, risk and operations.
Key Responsibilities- Work across areas such as retrieval and knowledge systems, multi‑agent orchestration, evaluation, reliability and context engineering.
- Take AI systems from early prototypes to trusted, production‑ready solutions.
- Own high‑impact projects from initial concept through to production.
- Partner with teams across the firm to identify problems and deliver scalable solutions.
- Turn team‑specific use cases into solutions that can be adopted more widely across the organisation.
- Strong software engineering background with modern AI technologies.
- Hands‑on experience building LLM‑powered systems in production, including agents, RAG pipelines, MCPs, tool‑use and multi‑step workflows.
- Familiarity with frameworks such as LangGraph, Pydantic AI or similar.
- Strong Python engineering skills and ability to produce clean, testable, production‑quality code.
- Experience with context engineering: retrieval strategies, prompt construction, information routing, memory.
- Experience with evaluation and observability for AI systems, including measuring accuracy, detecting regressions and understanding failure modes.
- Ability to work across domains; comfortable embedding with quantitative research teams one month and ops teams the next.
- Clear communication skills.
- Experience with fine‑tuning or adapting foundation models (e.g., LoRA, DPO).
- Comfort integrating with heterogeneous stacks such as C#, C++, JVM, gRPC, Kubernetes.
- Contributions to open‑source AI projects, technical writing or conference talks.
- Highly competitive compensation plus annual discretionary bonus.
- Lunch provided (via Just Eat for Business) and dedicated barista bar.
- 35 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.
AI Engineer employer: Barlowe LLP
At G-Research, we pride ourselves on being an exceptional employer, offering a dynamic work environment in the heart of London where innovation thrives. Our commitment to employee growth is evident through our collaborative culture, competitive benefits including 35 days of annual leave and a generous pension scheme, and opportunities to work on high-impact AI projects that shape the future of quantitative finance. Join us to be part of a diverse team that values your ideas and fosters a healthy work/life balance.
StudySmarter Expert Advice🤫
We think this is how you could land AI Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with potential colleagues on LinkedIn. We all know that sometimes it’s not just what you know, but who you know that can help you land that AI Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving LLM-powered systems or context engineering. We love seeing practical examples of your work, so make sure to highlight any contributions to open-source AI projects too!
✨Tip Number 3
Prepare for interviews by brushing up on your Python skills and understanding the latest AI technologies. We recommend practicing coding challenges and discussing your thought process out loud, as clear communication is key in our 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, we’re always on the lookout for passionate candidates who are eager to tackle complex problems in quantitative finance.
We think you need these skills to ace AI Engineer
Some tips for your application 🫡
Show Off Your Skills:Make sure to highlight your strong software engineering background and hands-on experience with AI technologies. We want to see how you've built LLM-powered systems in production, so don’t hold back on the details!
Tailor Your Application:Take a moment to customise your application for the AI Engineer role. Mention specific projects or experiences that align with our key responsibilities, like context engineering or multi-agent orchestration. It shows us you’re genuinely interested!
Be Clear and Concise:When writing your application, clarity is key! Use straightforward language to explain your experiences and skills. We appreciate clear communication, so make it easy for us to understand your journey.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy – just follow the prompts!
How to prepare for a job interview at Barlowe LLP
✨Know Your AI Stuff
Make sure you brush up on your knowledge of modern AI technologies and frameworks like LangGraph and Pydantic AI. Be ready to discuss your hands-on experience with LLM-powered systems and how you've tackled challenges in building production-ready solutions.
✨Showcase Your Problem-Solving Skills
Prepare examples of high-impact projects you've owned from concept to production. Think about how you identified problems and delivered scalable solutions, especially in collaboration with different teams. This will demonstrate your ability to work across domains.
✨Communicate Clearly
Since clear communication is key, practice explaining complex technical concepts in simple terms. You might be asked to describe your approach to context engineering or evaluation strategies, so make sure you can articulate your thought process effectively.
✨Be Ready for Technical Questions
Expect questions that dive deep into your Python skills and your experience with clean, testable code. Brush up on your knowledge of integrating with various stacks like C#, C++, and Kubernetes, as well as your familiarity with fine-tuning foundation models.
