Forward Deployed Engineer

About the role

<p><strong>Department Overview:</strong></p> <p>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.&nbsp;</p> <p>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.</p> <p>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.&nbsp;</p> <p>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.</p> <p>&nbsp;</p> <p><strong>Key Responsibilities</strong></p> <p><strong>Embedded delivery</strong>&nbsp;</p> <ul> <li>Scope pod use cases that need engineering build, working directly with PMs, traders and quants.&nbsp;</li> <li>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.&nbsp;</li> </ul> <p><strong>Engineering standards in the pod</strong>&nbsp;</p> <ul> <li>Pair with pod developers on live builds, sharing good practice in design, code review, testing and safe use of agentic coding tools.&nbsp;</li> <li>Leave the pod able to extend and maintain what has been built, rather than dependent on you.&nbsp;</li> </ul> <p><strong>Shared capability and reuse</strong>&nbsp;</p> <ul> <li>Turn repeated pod requirements into reusable MCP servers, skills, agents and templates, available for use across the firm.&nbsp;</li> <li>Feed engineering gaps and requirements directly into the team’s roadmap.</li> </ul> <p><strong>Governance and graduation</strong>&nbsp;</p> <ul> <li>Build entitlements, per-user identity, audit and evaluation into every solution from day one.&nbsp;</li> <li>Take successful applications through graduation to Technology ownership, including evals, documentation and operational handover.&nbsp;</li> </ul> <p><strong>Business outcomes</strong>&nbsp;</p> <ul> <li>Assist in tracking the business impact of what you deliver: time saved, risk reduced, decisions supported.&nbsp;</li> </ul> <p>&nbsp;</p> <p><strong>Key Competencies: </strong></p> <p>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.&nbsp;</p> <p>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.&nbsp;</p> <p>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.&nbsp;</p> <p>&nbsp;</p> <p><strong>Key Experience and Qualifications:&nbsp;</strong></p> <p>The ideal applicant will have:</p> <p><strong>Essential</strong>&nbsp;</p> <ul> <li>5+ years in software engineering, with strong Python and SQL.</li> <li>Direct front office experience at a hedge fund or bank, working day to day with traders or PMs, ideally in Rates or Emerging Markets.</li> <li>Hands-on experience building LLM applications: retrieval (RAG), tool use and agents, context engineering, and evaluation of output quality.</li> <li>Heavy daily use of agentic coding tools, with a clear view of where they fail and how to review their output.</li> <li>A track record of shipping produc

Skills