Join a dynamic team building AI infrastructure and applications that power sophisticated investment workflows for institutional clients. You'll develop LLM-driven features, agentic systems, and full-stack platforms that transform complex financial processes into efficient, production-ready tools. This role demands speed, technical depth, and a passion for how AI reshapes institutional finance.
Key Responsibilities:
- Build LLM-powered features into client platforms, including research intelligence, natural language queries, automated summarization, and agentic workflows
- Design agentic pipelines and integrations with data sources using modern AI frameworks
- Develop end-to-end full-stack applications for portfolio analytics, risk management, and research workflows
- Create high-performance backend APIs using Python frameworks like FastAPI
- Build responsive frontend interfaces in React for interacting with financial data
- Develop and maintain ETL pipelines for financial market data including positions, securities, and risk metrics
- Implement analytics layers using timeseries and linear algebra operations with tools like Pandas or Polars
- Deploy applications fluidly in Kubernetes environments for fast, reliable delivery
3-8 years of experience as a full-stack software engineer or applied AI engineer in institutional investing or fintech
Proven track record building user-facing products from 0-to-1 using agentic AI tooling
Hands-on experience with LLM APIs, agentic frameworks, and prompt engineering
Expertise in Python, including API development with FastAPI, Flask, or Django
Understanding of agentic loops in modern AI frameworks
Ability to thrive in unstructured environments and solve loosely defined problems
Active use of AI tools with conviction that AI transforms software development
Deep interest in how institutional investors operate and make decisions
Preferred:
Experience in quantitative fields like biotech or data-intensive environments
Familiarity with institutional investor operations from roles at leading firms
Track record of shipping software quickly based on user feedback
Kubernetes deployment experience in client environments
Background in financial analytics or risk management systems