Applied AI Engineer

Role Overview

We’re looking for software engineers to chart the course of how AI is reshaping institutional finance. You’ll build AI Infrastructure (observability, agent orchestration, expert skills, tools as CLI’s and MCP, data orchestration, and UI component libraries) that are leveraged by some of the world’s most sophisticated hedge funds as part of their AI implementations, working directly with their investment teams to turn complex workflows into elegant, production-grade applications.

This role sits at the intersection of AI implementation and financial software. You won’t just use AI tools – you’ll build AI-powered features directly into client platforms: LLM-driven research intelligence, agentic workflows, MCP-connected data sources, and automation layers that compress weeks of analyst work into seconds. The ideal candidate is a strong full-stack engineer who is fluent in modern AI tooling and deeply curious about how hedge funds and asset managers think, invest, and operate.

Speed is a core part of the job. Our model is to deliver fully customized platforms in weeks, not months, which means you need to ship with conviction, iterate based on real user feedback, and know when to build from scratch versus leverage proven infrastructure.

Key Responsibilities

- AI-Powered Feature Development: Build LLM-powered features directly into client-facing platforms, including research intelligence tools, natural language query layers, automated summarization, and agentic workflows that fundamentally change how investment teams work

- Agentic Tooling & MCP Integration: Design and implement MCP-connected data sources, agentic pipelines, and AI orchestration layers using frameworks like Claude Code, LangGraph, Open Claw, Open Code and similar, extending client platforms with live, intelligent data access

- Full-Stack Application Development: Build end-to-end applications tailored to each client’s unique portfolio analytics, risk management, and research workflows—from backend APIs to responsive frontends

- Backend Services: Design and maintain high-performance APIs using Python (FastAPI or similar) that power client-specific data access, analytics, and AI inference

- Frontend Development: Build intuitive, responsive user interfaces in React that enable investment teams to interact with complex financial data clearly and efficiently

- Data Pipeline Development: Build and maintain ETL pipelines that handle critical financial market data—positions, securities, risk metrics, and research signals—with reliability and performance

- Financial Analytics: Implement analytics layers for performance and risk calculations using timeseries and linear algebra operations (Pandas, Polars)

- Ship Fast, Iterate Often: Deliver working software in compressed timelines, gather direct feedback from hedge fund users, and continuously improve, treating speed and quality as complementary, not competing

- Kubernetes Deployments: Be able to work fluidly with Kubernetes within each client environment to be able to ship fast and reliable.