Applied AI Engineer | Python | LLM Agents | MCP | Finance Domain | Must Have Startup Experience
Location: New York, NY (Hybrid)
Package: $210,000 – $250,000 + competitive equity
Eligibility: Open to candidates with existing US work authorisation - US Residents Only
Tech Stack: Python | React | SQL | Azure | LLM APIs | MCP | Agentic Frameworks (All Required)
🚨 Please only apply if you have commercial experience with ALL of the following 🚨
- Python backend engineering (FastAPI, Flask, or Django)
- Building AI agents, LLM-powered tools, or agentic workflows
- Hands-on experience with agentic frameworks (LangChain, LangGraph, Claude Code, or similar)
- Production-grade, customer-facing software development
- Startup or scale-up experience
- Finance, fintech, or quantitative/data-first domain background
🚀 Join a profitable, investor-independent AI company at a genuine inflection point - post-PMF, scaling fast, and building the kind of product that makes candidates stop scrolling. This team is deploying production AI systems into some of the most demanding, data-intensive environments in the world. You won't be doing demos or maintaining legacy code. You'll be shipping real AI infrastructure that compresses complex, high-stakes workflows from weeks into seconds - and the people using it will feel it immediately.
This is a 0-to-1 role in the truest sense. Every project starts from first principles and ships into a live client environment. If you want to work at the frontier of applied AI and genuinely care about how institutional finance thinks and moves - this is the seat.
ℹ️ Very Important Notes
- This is a hands-on applied engineering role — not suitable for research-only or model training profiles
- You must be comfortable building user-facing products, not just internal tooling
- Direct client interaction is part of the job — you'll work alongside end-users to understand and automate real workflows
- High autonomy expected - design, build, ship, iterate, repeat
Required Background
- 3–8 years of full-stack or applied AI engineering experience
- Proven delivery of production systems used by real end-users
- Background in fintech, institutional finance, or other data-first, quantitative fields
- Strong CS fundamentals with a founding-engineer mindset
- First-principles understanding of the agentic loop and how modern AI frameworks operate
Must-Haves
- Strong Python backend development - FastAPI, Flask, or Django
- Hands-on experience building with LLM APIs, MCP servers, and agentic frameworks
- Full-stack capability - backend APIs through to React frontends
- Experience building and maintaining ETL/data pipelines for financial or complex structured data
- Ability to work independently, handle ambiguity, and ship fast
- Genuine conviction that AI is transforming software - active daily use of AI tooling
- Deep curiosity about how data-driven, quantitative industries think and operate
Bonus Experience
- Background at a top-tier hedge fund, asset manager, or fintech (buy-side or sell-side)
- Past technical founder or early founding engineer
- Experience with Kubernetes deployments in client environments
- Open-source contributions in AI or financial tooling
- Financial analytics experience - timeseries, risk, performance (Pandas, Polars)
Hands-On Experience With
- Python (FastAPI/Flask/Django backend systems)
- LLM APIs, MCP-connected data sources, agentic pipelines and orchestration layers
- React (responsive, user-facing frontends)
- Azure cloud infrastructure
- SQL and financial data pipelines
What You'll Be Doing
AI-Powered Feature Development
- Build LLM-powered features directly into client platforms - research intelligence, natural language query, automated summarisation, agentic workflows
- Design and implement MCP-connected data sources and AI orchestration layers
Full-Stack Application Development
- Build end-to-end applications tailored to each client's unique data and workflow requirements
- Maintain high-performance backend APIs and intuitive React frontends
Data & Infrastructure
- Build and maintain ETL pipelines handling complex, high-value structured data
- Work fluidly with Kubernetes to ship fast and reliably inside client environments
Ship Fast, Iterate Often
- Deliver working software in compressed timelines
- Gather direct feedback from sophisticated end-users and continuously improve
What They're Looking For
- A technically sharp, scrappy engineer who thinks like a founder
- Someone comfortable owning multiple workstreams in a fast-moving environment
- A builder who thrives on autonomy, high ownership, and rapid iteration
- An engineer who enjoys combining deep technical execution with real-world client insight
If you meet the above and want to build production AI systems at the frontier of applied AI - get in touch for a fast response.