Applied AI Engineer | Python | LLM Agents | MCP | Finance Domain Full Time

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.