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 roleThe Applied AI team is a centralised engineering team within the AI Engineering Department. We build, adopt, and maintain the abstracted agentic tools, platforms, and SDKs that enable intelligent systems across G‑Research.
We don't just build the platform — we use it ourselves to deliver high‑impact solutions, proving the patterns work and feeding real‑world lessons back into the tooling.
As an AI Engineer you will work across four dimensions:
- Platform & SDKs — Build and maintain G‑Research's agentic platform, evaluation tooling, and Python SDKs that abstract away infrastructure complexity so teams across the firm can build agents quickly and safely.
- Solutions — Use the platform to deliver production agentic workflows for research and corporate stakeholders, validating the platform through real use cases.
- Ways of working — Define and champion best practices for building agents at G‑Research: evaluation standards, development patterns, testing approaches, and reference architectures. Lead by example.
- Embedded delivery — When needed, deploy into specific business teams for weeks at a time to deeply understand their domain, solve critical problems, and uncover new AI opportunities first-hand.
Key responsibilities of the role include:
- Build and evolve the agentic AI platform — develop the core abstractions, orchestration patterns, and infrastructure that enable agent development across G‑Research.
- Create and maintain Python SDKs with G‑Research-specific abstractions that simplify common patterns: agent scaffolding, tool integration, context management, evaluation, and deployment.
- Adopt and integrate best-in-class open-source tooling (LangGraph, Pydantic AI, and emerging frameworks), wrapping them in firm-specific abstractions rather than reinventing the wheel.
- Define ways of working for agent development — establish evaluation standards, development patterns, testing approaches, and reference architectures that teams across the firm follow.
- Lead by example on agentic evaluations — build and operate evaluation pipelines (e.g. LangSmith, Langfuse) that set the standard for how agents are measured, monitored, and improved at G‑Research.
- Deliver production agentic solutions for internal stakeholders, using the platform to solve real problems and validating that the abstractions work under real-world conditions.
- Embed with business teams across the firm — partner directly with research and corporate teams to solve critical problems and identify new AI opportunities. This may involve deploying into a specific team for several weeks to deeply understand their domain and deliver tailored solutions.
- Apply context engineering techniques to optimise how agents retrieve, structure, and utilise information — and codify those techniques into the platform and SDKs.
- Where needed, fine‑tune and optimise models (parameter‑efficient or full‑weight) to meet domain‑specific accuracy, latency, and cost targets.
- Integrate with existing stacks (C#, C++, JVM) ensuring clear APIs, monitoring, and CI/CD pipelines.
- Upskill engineers across the firm through pair‑programming, workshops, SDK documentation, and written playbooks on agent development best practices.
- Stay on top of the LLM ecosystem (tooling, evaluation techniques, open‑source releases) and feed lessons learned back into the platform and wider AI Engineering Department.
We value pragmatic engineers who combine deep technical ability with strong product intuition and impeccable stakeholder communication. You should enjoy moving between green‑field proofs‑of‑concept and hardening them into resilient, audited services.
Essential
AI Engineering
- Hands‑on experience building LLM applications with LangGraph/LangChain, Pydantic AI, FastAPI, MCPs, and RAG (pgvector, Pinecone, Qdrant, Milvus, etc.).
- Strong understanding of context engineering — retrieval strategies, context window management, dynamic prompt construction, and information routing.
- Experience designing complex agentic workflows including multi-step planning, tool use, self-correction, and multi-agent patterns.
- Solid understanding of RAG patterns, prompt engineering, and safe deployment considerations.
Evaluation & Observability
- Experience with agentic evaluation frameworks (e.g. LangSmith, Langfuse) for measuring accuracy, latency, cost, and detecting behavioural regressions.
Platform & Software Engineering
- Proven expertise in Python for production systems, with fluency in modern async patterns, typing, and testing frameworks.
- Experience building platform-level software — reusable APIs, shared libraries, SDKs, extensible architectures — not just one-off solutions.
- Comfort integrating with heterogeneous tech stacks (REST/gRPC, message buses, SQL/NoSQL stores) and automating deployment with Git, Docker, and Kubernetes.
Communication
- Ability to translate ambiguous requirements into clear technical plans and to communicate trade‑offs to both technical and non‑technical audiences.
Desirable
- Exposure to enterprise security, data‑privacy, and model‑governance frameworks.
- Demonstrable skill fine‑tuning or parameter‑efficiently adapting foundation models (LoRA, QLoRA, DPO, etc.) and evaluating their performance.
- Experience running low‑latency inference on‑prem GPU clusters or hybrid cloud environments.
- Knowledge of experiment‑tracking, offline evaluation, and A/B‑testing pipelines for LLM applications.
- Experience building chat or agent UIs for end-user interaction with agentic systems.
- Contributions to open‑source AI‑engineering projects or publication of technical blogs/talks.
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 special need that requires accommodation please let us know in the relevant section
