1 month ago
London, United KingdomMid Level / Senior
Responsibilities
- Write maintainable code and contribute to version control, CI/CD pipelines, testing, documentation, and firmwide engineering practices.
- Build, deploy, and maintain internal tools and dashboards for quantitative outputs, portfolio analytics, and market data.
- Implement and operate AI capabilities using LLM APIs, MCP servers, agentic workflows, and related architectures for Fundamental Equity.
- Work with quant researchers to turn analytical prototypes into reliable, production-ready applications.
- Identify opportunities for AI tooling to improve productivity, output quality, and investment-team capabilities.
- Own projects end-to-end from scoping and delivery through maintenance and iteration.
Requirements
- 2–5 years of experience in quantitative development, dev strats, analytics engineering, product management, or quantitative analysis in a relevant financial or data-provider organization.
- Strong Python proficiency and solid SQL and database usage skills.
- Production experience building dashboards and user interfaces actively used by investment teams or professional users.
- Hands-on experience integrating AI and LLMs through APIs, prompt engineering, tool use, and retrieval-augmented workflows.
- Practical familiarity with MCP servers, agentic frameworks, or multi-model orchestration architectures.
- Experience using AI-assisted development tools such as Claude Code or Cursor in daily engineering work.
- Sound software engineering fundamentals including structured codebases, version control, CI/CD pipelines, documentation, and testing.
- Ability to work collaboratively with quant researchers, central Technology teams, and investment professionals in a business-facing environment.
- Product-minded, proactive, self-starting approach with intellectual curiosity about financial markets and the investment process.
- Preferred: alternative data ingestion or vendor API integration experience; hedge fund or multi-manager experience; AWS, EC2, containerized workloads, Terraform or similar infrastructure-as-code tooling; Docker, Kubernetes, and Prefect, Airflow, or equivalent workflow orchestration experience.
Benefits
- Learning and educational offerings and opportunities to make an impact.
- Internal networks, external partnerships, and service initiatives supporting inclusion and community.
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