Build and operate end-to-end agentic AI products from initial concept through production and product-market fit. Responsibilities include developing agents, retrieval and orchestration systems, integrating tools, shaping product direction with design and product partners, hardening systems after launch, and creating reusable engineering primitives. The role requires entrepreneurial ownership, production AI experience, strong engineering judgment, and the ability to work effectively with technical and commercial stakeholders.
Build the thing, then build the company around it.
Our client backs and builds AI-native ventures from the ground up, and they're looking for a Senior AI Engineer who wants to be a core builder, not employee #400. You'll take validated concepts and turn them into real products with real users, then keep going as those products find their footing. If you've shipped end-to-end AI systems to production, built things nobody asked you to build, and want genuine ownership in what you create, keep reading.
You're probably a strong engineer from a demanding technical environment with an entrepreneurial itch. Someone who's tired of incremental work on someone else's platform and wants to see their own fingerprints on something that ships.
What you'll do
- Build end-to-end agentic AI systems: agents, retrieval, orchestration, tool integrations. From a blank repo through product-market fit
- Take a product from rough scope to shipped, cutting hard to the smallest version that proves value, then hardening it for production
- Pull reusable patterns and primitives out of each build so the next one starts further down the road
- Work shoulder-to-shoulder with product and design partners to shape what gets built, not just how
- Move fluidly across very different product contexts, and treat ambiguity as the job rather than an obstacle
Requirements
What you bring
- Several years of experience at an environment known for engineering rigor. A top quantitative trading firm or a high-bar technology company, with individual output you can clearly point to as your own
- A track record shipping AI systems to production and operating them after launch: real users, real failure modes, a real post-launch story. Not demos, notebooks, or research prototypes.
- A visible entrepreneurial streak: a side project, a startup, a founding or early role, or a genuine 0→1 effort inside a larger org. Evidence you don't just execute what's handed to you.
- A STEM degree (CS, Math, Physics, Engineering) from a strong program.
- Commercial EQ: you can hold your own with non-technical stakeholders, customers, and partners, not only other engineers.
Tech you'll work with
Python, LLMs, agentic AI frameworks, RAG and retrieval systems, model serving, evaluation and regression suites, CI/CD, observability and monitoring.
Who tends to thrive here
- Engineers from AI-native startups, or founding/early engineers who've shipped real product to real users
- Quant or quant-adjacent engineers with an entrepreneurial background who want to move into building product
Probably not the right fit
- Researchers who want to build models with no commercial responsibility or product ownership.
- Engineers whose experience is incremental improvements to established systems, with no 0→1 signal.
- Short-tenure pattern.
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What you need to know about the NYC Tech Scene
As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.
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