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.

Learn more about this Employer on their Career Site