AI Engineer

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 Applied AI team acts as an internal strike force: small, high-impact groups that embed directly within business units, research groups and platform teams to accelerate the adoption of AI techniques across the firm.

As a Forward-Deployed AI Engineer you will turn LLM capabilities into production solutions that drive measurable value, from intelligent workflows to research agents.

Your day-to-day will alternate between rapid prototyping, integrating with existing systems and coaching domain experts on best practices for maintainable, safe LLM applications. Success is measured by the speed and robustness with which internal partners can ship and own new AI-based features.

Key responsibilities of the role include:

• Engaging directly with internal clients to understand pain points, identify AI opportunities and shape solution roadmaps
• Building end-to-end AI-powered systems in Python using LangGraph, and similar orchestration frameworks, FastAPI, MCPs and vector-based retrieval services
• Fine-tuning and optimising models (parameter-efficient or full-weight) to meet domain-specific accuracy, latency and cost targets
• Designing RAG and agentic workflows that safely combine proprietary data with public and on prem models
• Integrating new services with existing C#, C++ or JVM-based stacks, ensuring clear APIs, monitoring and CI/CD pipelines.
• Establishing repeatable patterns, such as reference architectures, templated infra testing harnesses, that enable teams to self-serve future use-cases
• Upskilling engineers and analysts through pair-programming, workshops and written playbooks on AI engineering best practices
• Staying on top of the LLM ecosystem, including tooling, evaluation techniques and open-source releases, and feeding lessons learned back into the wider AI Engineering Department

Who are we looking for?

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.

The ideal candidate will have the following skills and experience:

• Proven expertise in Python for production systems, with fluency in modern async patterns, typing and testing frameworks
• Hands-on experience building Agentic AI applications with LangGraph and LangChain, Pydantic, FastAPI, MCPs and RAG, such as pgvector, Pinecone, Qdrant and Milvus
• Demonstrable skill fine-tuning or parameter-efficiently adapting foundation models, such as LoRA, QLoRA and DPO, and evaluating their performance
• Solid understanding of RAG patterns, prompt engineering, evaluation metrics and safe deployment considerations
• Comfort integrating with heterogeneous tech stacks, such as REST/gRPC, message buses and SQL/NoSQL stores, and automating their deployment with Git, Docker and Kubernetes
• 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
• 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
• Contributions to open-source AI-engineering projects or publication of technical blogs and talks

Why should you apply?

• 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