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G-Research

Quantitative finance research applying rigorous methods

Applied AI Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeLondon, UKIn-person

About the Job

Requirements

  • Hands-on experience building large language model systems in production, including agents, retrieval-augmented generation pipelines, Model Context Protocols, tool use, and multi-step workflows.
  • Experience using frameworks such as LangGraph, Pydantic AI, or similar frameworks.
  • Strong Python engineering skills, including clean, testable, production-quality code.
  • Experience with context engineering, including retrieval strategies, prompt construction, information routing, and memory.
  • Experience with evaluation and observability for artificial intelligence systems, including measuring accuracy, detecting regressions, and understanding failure modes.
  • Ability to work across domains and embed with quantitative research and operations teams.
  • Clear communication skills.
  • Experience fine-tuning or adapting foundation models, such as with Low-Rank Adaptation and Direct Preference Optimization.
  • Experience integrating with heterogeneous technology stacks, including C#, C++, Java Virtual Machine technologies, gRPC, and Kubernetes.

Responsibilities

  • Work across retrieval and knowledge systems, multi-agent orchestration, evaluation and reliability, and context engineering.
  • Take artificial intelligence systems from early prototypes to trusted, production-ready solutions.
  • Own high-impact projects from initial concept through 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.

Desired Qualifications

  • Contributions to open-source artificial intelligence projects, technical writing, or conference talks.

About the Company

G-Research

Quantitative finance research applying rigorous methods

G-Research applies scientific rigour to solve complex challenges in financial markets. It brings together world-class researchers and engineers to develop data-driven models and trading ideas, testing them through rigorous experimentation and robust infrastructure. Unlike many traditional asset managers, its culture emphasizes deep exploration, reproducibility, methodical execution, and long-term thinking. The goal is to generate alpha through disciplined, evidence-based research that informs investment decisions and advances understanding of financial markets.

Company size
1,001-5,000
Company stage
Not listed
Total funding
Not listed
Headquarters
London, United Kingdom
Founded
2001

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