AI Engineer

Unable to sponsor. Seeking candidates working in the Capital Markets, Wall Street firms.

Job Title - AI LEAD Engineer - Forward Deployed Engineer
Location - New York, NY ( 4 days onsite with the client, 1 day remote)
Duration - 12+ Months

The Work: You’d embed with a hedge fund or investment management firm 4 days a week as the technical lead on the deployment. Your job is to customize our products to fit their environment and ship the workflows they use every day, from backend integrations to the interfaces users touch. Some of our products drop in cleanly. Others need real engineering to fit the client’s setup. Your judgment decides the mix.

You stay with one deployment long enough to see the work mature. You’re not rotating through customer logos and you’re not handing off to a platform team. The feedback loop is short. You’ll see what works and what doesn’t from the people who actually use the system, not from a roadmap committee three quarters out.
FDE work is heavy on building. Most weeks land around 80 percent building, including the planning and decision-making that are now part of how modern AI software gets made. The rest is working across the client firm with portfolio managers, analysts, traders, ops, compliance, and tech leadership.

You Should Have

• 5+ years of production software engineering. You’ve built systems that businesses run on every day, and you can walk us through the architecture decisions, the failure modes you hit at scale, and how you fixed them.
• Full-stack delivery. Strong Python and TypeScript, with React experience on the user-facing side. Our deployments end in interfaces that portfolio managers and analysts use daily, and you own that last mile.
• Engineering process discipline. You design APIs and integrations that production systems depend on, and you bring SDLC rigor with you: code review, testing, versioned releases, documented decisions.
• Hands-on production AI work: agents, retrieval, evaluation suites, human-in-the-loop review. You’ve shipped LLM-powered workflows to real users and iterated on how they failed. We care more about what you’ve shipped than the calendar.
• You know real-world RAG is more about chunking, reranking, and grounding evaluation than picking a vector DB.
• You’ve made the call between off-the-shelf agent frameworks and custom orchestration, and you can defend the choice.
• Track record of owning a bounded client engagement from kickoff to production within 90 to 180 days, including scope negotiation, written milestone delivery, and stakeholder management across business, operations, and compliance.
• Shipped in a regulated or operationally complex environment where audit trails, evidence, and human-in-the-loop governance are first-class requirements, not afterthoughts.
• Comfort being measured on deployment outcomes: time-to-first-value, production reliability, user activation, and whether the client will take a reference call.
• The disposition to own a customer-facing deployment end to end.

On Domain Experience - Financial services experience is not required, but it’s preferred. If you haven’t worked in this domain before, show us you’ve learned a complex one quickly and we’ll take that seriously.

It Helps If You

• Are currently somewhere a portfolio manager would recognize. Hedge fund, alternative asset manager, major investment bank, or AI-for-finance startup.
• Have been the embedded engineer at a prior enterprise software company. FDE, solutions engineer, deployed engineer, whatever the title was.
• Build with AI on the side. Side projects, open source, agent experiments you’ve actually put into the world.
• Have worked in a highly regulated or operationally complex environment.