AI Engineer

AI Engineer

G-Research

London, United Kingdom

About the Company

G-Research tackles complex quantitative finance problems through scientific approaches. The firm unites world-class researchers and engineers in an environment that values deep exploration and focuses on building platforms for high-impact research.

The Role

The Applied AI team builds, adopts, and maintains abstracted agentic tools, platforms, and SDKs that enable intelligent systems across the firm. This AI Engineer role spans four dimensions:

Platform & SDKs: Build agentic platforms, evaluation tooling, and Python SDKs abstracting infrastructure complexity. Create core abstractions for agent development and orchestration with firm-specific abstractions.

Solutions: Deliver production agentic workflows validating the platform.

Ways of Working: Define best practices for agent development across the firm. Establish evaluation standards using LangSmith and Langfuse.

Embedded Delivery: Deploy into business teams for weeks to solve critical problems and upskill engineers firm-wide.

Specific duties include developing core abstractions, creating Python SDKs, adopting open-source tools (LangGraph, Pydantic AI), establishing evaluation standards, delivering production solutions, and designing complex agentic workflows with multi-step planning, tool integration, and self-correction patterns.

Essential Requirements

• Hands-on experience building LLM applications with LangGraph/LangChain, Pydantic AI, FastAPI, MCPs, and RAG
• Strong context engineering understanding (retrieval strategies, prompt construction, routing)
• Experience designing complex agentic workflows
• Evaluation framework experience (LangSmith, Langfuse)
• Production Python expertise with async patterns and testing
• Platform-level software development (APIs, SDKs, extensible architectures)
• Integration with REST/gRPC, message buses, SQL/NoSQL, Docker, Kubernetes
• Clear communication skills translating requirements into technical plans

Desirable Qualifications

• Enterprise security and data privacy knowledge
• Model fine-tuning experience (LoRA, QLoRA, DPO)
• Low-latency GPU cluster inference optimization
• Experiment-tracking and A/B-testing pipeline knowledge for LLM applications
• Chat/agent UI development experience
• Open-source contributions or technical publications

Benefits

• Competitive compensation with annual discretionary bonus
• 30 days annual leave
• 9% pension contributions
• Lunch provided and barista bar
• Comprehensive healthcare and life assurance
• Flexible work environment