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

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