Artificial Intelligence Engineer

AI Engineer - Institutional FinTech New York City Compensation: Fixed $200k - $250k + Bonus+ Equity The Company AI platform for institutional investment management — deployed natively inside hedge fund and asset manager environments. The platform automates research workflows, builds agentic connectors across financial data sources, and compresses weeks of analyst work into minutes. Bootstrapped to $10M+ ARR with a core team from Citadel, Goldman Sachs, Millennium, and D.E. Shaw. The Role As an AI Engineer you will build and deploy bespoke AI platforms directly inside hedge fund client environments — their data, their infrastructure, their workflows. Every engagement is greenfield. There is no legacy codebase to maintain, no narrow feature lane, no recycled solutions. You take a problem from first principles and turn it into a working production system You will work directly with investment professionals — PMs, research analysts, quant teams — understand how they operate, and build AI that fundamentally changes how they work. LLM-powered research intelligence, agentic workflows, MCP-connected data sources, full-stack applications tailored to each client's portfolio analytics and research processes. What You'll Build LLM-powered features directly into client-facing platforms — research intelligence tools, natural language query layers, automated summarization, agentic workflows MCP-connected data sources and agentic pipelines using frameworks including LangGraph, Claude Code, and LangChain Full-stack applications from backend APIs to responsive frontends, tailored to each client's unique investment workflows High-performance Python APIs (FastAPI) powering client-specific data access, analytics, and AI inference ETL pipelines handling critical financial market data — positions, securities, risk metrics, research signals — with reliability and performance Analytics layers for performance and risk calculations using timeseries and linear algebra operations What We're Looking For 3–8 years as a full-stack software engineer or applied AI engineer. Must have built production AI systems shipped to real users — not prototypes Must have full-stack delivery experience — backend to frontend, end-to-end ownership Must have built with MCPs, LLM APIs, and agentic frameworks in production Must have data pipeline experience handling complex structured financial data Demonstrated track record using agentic AI tooling effectively — Claude Code, Codex, MCP servers — and building user-facing products from 0-to-1 Institutional finance or fintech exposure strongly preferred Candidate Archetypes A — A full-stack AI engineer at a financial data platform or institutional fintech who has built production LLM features on top of complex financial data infrastructure and wants to work closer to the investment decision layer. B — A software engineer at a hedge fund, asset manager, or bank who has been building internal AI tooling for investment teams and wants to own the full product rather than one component of an internal platform. C — An AI Engineer at an enterprise AI company or data driven SAAS who has shipped agentic systems. Show more Show less