The Global Risk Analytics team is looking for a seasoned Quantitative Risk Developer to join our Quant Risk Development team. This role offers the opportunity to work closely with other risk analytics teams, including Market Risk , Credit Risk , and RegIM , to design and operate AI-powered systems that automate complex risk workflows and support regulatory submissions. The ideal candidate brings equal depth in Agentic Coding and financial risk domain knowledge , with hands-on experience structuring agentic workflows, validating AI-generated output, and architecting end-to-end systems in environments similar to Claude Code.
Key Responsibilities
- Design and implement end-to-end agentic workflows that enable autonomous planning, multi-step execution, and tool use across risk analytics and regulatory submission processes.
- Architect and own the full system design of AI-powered risk platforms, including data flow, tool integration, orchestration layer, and production deployment.
- Build and maintain validation frameworks and testing pipelines to ensure the correctness and reliability of AI-generated code and analytical outputs in a financial risk context.
- Develop and integrate LLM-powered developer tooling - including CLI-based agents and code generation/review pipelines - to accelerate risk analytics delivery.
- Collaborate with Market Risk, Credit Risk, and RegIM teams to translate complex domain requirements into robust, automated Python solutions.
- Develop scalable, reusable Python libraries and contribute to a robust CI/CD environment through testing, peer review, and version control.
- Facilitate efficient data processing, integration, and reporting pipelines to streamline regulatory submissions and internal analytics.
Required Qualifications
- Education: Master's degree in Financial Engineering, Mathematics, Computer Science, or a related quantitative field preferred; Bachelor's considered with exceptional experience.
- Experience: At least 5 years of professional experience in Python backend development for financial applications.
- Demonstrated experience designing and maintaining multi-step agentic workflows, orchestration logic, branching execution, and tool use - not limited to simple LLM API calls.
- Practical experience building validation frameworks or testing pipelines to verify AI-generated outputs in a professional setting; concrete examples required.
- Hands-on experience with LLM-powered developer tooling or systems similar to Claude Code, including CLI-based AI agents or LLM-assisted code generation and review pipelines.
- Proven ability to own full system architecture for agentic platforms, covering data flow, tool integration, orchestration, and deployment.