/roles — ROLE_477
Applied AI Engineer
Cash-flow-positive fintech building AI infrastructure and tailored apps for hedge funds and asset managers
The role
- COMP
- $200K - $250K
- EQUITY
- Competitive
- LOCATION
- New York
- WORKPLACE
- Hybrid
- EXPERIENCE
- 3 - 8 years
- VISA
- None, Visa transfers
- STACK
- Python, React, Azure, FastAPI, Django, Langchain, Kubernetes, Pandas
- INDUSTRY
- AI, Fintech, Finance, Financial Services, Enterprise, B2B
The company
A cash-flow-positive, client-funded company building AI platforms that hedge funds and other investment firms use to wire up their data and turn in-house expertise into working agents.
- STAGE
- early-stage
- TEAM
- ~25 people
- FOUNDED
- 2023
JD — the work
About the role
This is a full-stack engineering role where AI meets institutional finance. You would build shared AI infrastructure, from agent orchestration and monitoring to data pipelines and reusable interface components, and add LLM-powered capabilities straight into each client's platform while working alongside fund investment teams. The company turns around bespoke platforms for each client within weeks, so the job rewards engineers who ship decisively, learn from real users, and can judge when reusing proven pieces beats starting fresh.
What you'll do
- Add LLM-driven research tools, natural-language querying, summarization, and agent workflows to client platforms
- Connect client data sources to agents over MCP and build the pipelines and orchestration around them
- Deliver complete applications for each fund's portfolio analytics, risk, and research work, from Python APIs to React front ends
- Run dependable ETL pipelines for positions, risk measures, security data, and research signals
- Implement performance and risk analytics with time-series and linear algebra in Pandas or Polars
- Deploy on Kubernetes within each client's environment and keep releases fast and reliable
- Release in short cycles, learn from how fund users respond, and keep improving
What they're looking for
- 3 to 8 years of software engineering with strong full-stack skills
- Fluency with modern AI tooling, including LLMs, agent frameworks, and MCP
- Python backend work (FastAPI or Django) plus React on the front end
- Comfort with Kubernetes and cloud environments such as Azure
- Genuine interest in how investment firms make decisions and run day to day
- Good instincts on reusing proven infrastructure versus writing something new