Applied AI Engineer

Applied AI EngineerCompensation: $200,000–$250,000 + EquityBootstrapped AI Platform for Institutional Investors ($10M+ ARR)New York, NY — HYBRIDAbout the RoleMost AI companies are racing to raise the next round. This one is profitable. With $10M+ ARR, no external funding, and zero investor pressure dictating the roadmap, this bootstrapped software company has achieved something rare: genuine product-market fit, built entirely on revenue. They help some of the world's most sophisticated hedge funds and institutional investors become AI-native — and they're doing it with a lean, elite team that came out of Citadel, Goldman Sachs, Millennium, and D.E. Shaw.This is a true 0-to-1 engineering role. There's no legacy codebase to maintain, no internal tooling lane to stay in, and no PM writing specs. Every project starts from a real problem and ends with a working product shipped to users in weeks, not months. The work spans from building agentic AI pipelines and MCP-connected data infrastructure to designing React frontends that let investment teams interact with complex financial data clearly and efficiently.The engineering team operates at the frontier of what's currently possible in applied AI — integrating LLMs, agentic workflows, and MCP-connected intelligence into production-grade platforms that directly influence how institutional capital is deployed. If you genuinely love both AI and finance, this is the rare seat where both matter equally.What You'll DoBuild LLM-powered, client-facing features including research intelligence tools, natural language query layers, automated summarization, and agentic workflows that fundamentally change how investment teams workDesign and implement MCP-connected data sources, agentic pipelines, and AI orchestration layers using frameworks such as Claude Code, LangGraph, and OpenClawBuild full-stack applications end-to-end — from Python (FastAPI) backend APIs powering data access and AI inference, to responsive React frontends tailored to portfolio analytics, risk management, and research workflowsBuild and maintain ETL pipelines handling critical financial market data — positions, securities, risk metrics, and research signals — with high reliability and performanceImplement analytics layers for performance and risk calculations using timeseries and linear algebra operations (Pandas, Polars)Deploy and maintain services fluidly within Kubernetes environments across each client's unique infrastructureShip fast and iterate often — deliver working software in compressed timelines, gather direct feedback from hedge fund users, and treat speed and quality as complementaryWhy This Seat$10M+ ARR, bootstrapped and profitable — no VC milestones, no investor pressure, just product and revenue0-to-1 in the truest sense: every project is a new, unsolved problem — agent health scoring, AI-powered handoff documents, orchestrator systems, built from scratchEngineering team alumni of Citadel, Goldman Sachs, Millennium, and D.E. Shaw — the calibre of people you'll learn from is rareGenuine ownership with no bureaucracy — broad scope, limited top-down direction, trusted to drive decisions and outcomesR&D culture within a commercial business: engineers actively shape the roadmap as AI capabilities evolveTechnology is the revenue core here — not a support function. The mission is to redefine how institutional investing is powered by AI$200K–$250K base with equityYou'll Thrive Here If YouHave 3–8 years of experience as a full-stack software engineer or applied AI engineer, ideally with exposure to institutional finance or fintechHave a demonstrated track record of using agentic AI tooling in practice — Claude Code, Codex, MCP servers — and have built user-facing products from 0-to-1Are a strong Python engineer — FastAPI, Flask, or Django experience is highly preferred and Python itself is non-negotiableHave a first-principles understanding of the agentic loop underlying most modern agentic frameworks (Claude Code, Codex, Cline, Open Code, etc.)Have hands-on experience building with LLM APIs, MCP servers, agentic frameworks, and prompt engineering — not just using AI tools, but shipping with themGenuinely find finance interesting — you understand how institutional investors think, make decisions, and use technology, and this world excites youThrive in unstructured environments where the problem definition is as much your job as the solutionNice-to-HavesDirect experience at an institutional investor or quant firm — Two Sigma, D.E. Shaw, Citadel, Point72, Addepar, or similar data-first environmentsExperience with timeseries data, financial analytics, or quantitative workflows (Pandas, Polars, NumPy)Kubernetes deployment experience in production, multi-client environmentsBackground in health/bio tech or other data-first quantitative fieldsWhat We Offer$200,000–$250,000 base salaryEquityHybrid work — New York City (Midtown)Small, high-calibre team with no layers of bureaucracyGenuine ownership and scope from day oneMust-Haves (Logistics)Based in or willing to relocate to New York City — hybrid roleStrong Python engineering skills (non-negotiable)Demonstrated hands-on use of agentic AI tooling in shipped, user-facing productsGenuine interest in institutional finance and how investment teams operate3–8 years of full-stack or applied AI engineering experienceThis is a product-facing role — candidates whose background is primarily model training, fine-tuning, internal tooling, or pure Big Tech structured environments are NOT a fit