Forward Deployed Engineer

Department Overview: The AI Team, part of Front Office Technology, builds the governed foundation for AI across the firm: secure access to firm data and knowledge, standard tooling, reusable AI services, and the controls that let AI be used safely in a regulated trading environment. Our model is simple. Pods build close to the problem, Technology provides the paved road, and successful applications graduate into formal Technology ownership as they grow in importance. The AI Team, part of Front Office Technology, builds the governed foundation for AI across the firm: secure access to firm data and knowledge, standard tooling, reusable AI services, and the controls that let AI be used safely in a regulated trading environment. Our model is simple. Pods build close to the problem, Technology provides the paved road, and successful applications graduate into formal Technology ownership as they grow in importance. Key Responsibilities Embedded delivery • Scope pod use cases that need engineering build, working directly with PMs, traders and quants. • Build production-grade applications using AI-assisted development (e.g. Claude Code) on the firm’s standard AI tooling: templates, CI/CD, the Institutional Knowledge Layer, MCP services and shared libraries. Engineering standards in the pod • Pair with pod developers on live builds, sharing good practice in design, code review, testing and safe use of agentic coding tools. • Leave the pod able to extend and maintain what has been built, rather than dependent on you. Shared capability and reuse • Turn repeated pod requirements into reusable MCP servers, skills, agents and templates, available for use across the firm. • Feed engineering gaps and requirements directly into the team’s roadmap. Governance and graduation • Build entitlements, per-user identity, audit and evaluation into every solution from day one. • Take successful applications through graduation to Technology ownership, including evals, documentation and operational handover. Business outcomes • Assist in tracking the business impact of what you deliver: time saved, risk reduced, decisions supported. Key Competencies: You are an experienced engineer who is credible on a trading desk. You understand how PMs and traders work, speak their language, and are comfortable challenging requests in order to deliver what actually adds value. You build quickly with AI tools but hold yourself to production standards: tested, governed, maintainable and reviewable. You understand the limitations of LLMs and design around them rather than over-promising. You look beyond the immediate request, spotting when a pod’s problem is really a firm-wide one and building it once so others can reuse it. You are comfortable with ambiguity, take ownership, and measure success by business outcomes. Key Experience and Qualifications: The ideal applicant will have: Essential • 5+ years in software engineering, with strong Python and SQL. • Direct front office experience at a hedge fund or bank, working day to day with traders or PMs, ideally in Rates or Emerging Markets. • Hands-on experience building LLM applications: retrieval (RAG), tool use and agents, context engineering, and evaluation of output quality. • Heavy daily use of agentic coding tools, with a clear view of where they fail and how to review their output. • A track record of shipping production systems: APIs, containers, CI/CD and cloud (Azure or Kubernetes preferred). • The ability to turn loosely defined desk requests into scoped deliverables, and the confidence to push back when something should not be built. Desirable • Building MCP servers or similar tool integrations. • Market data and time-series work (e.g. Bloomberg, Snowflake). • Integrating where re