Senior Full-Stack Software Engineer, AI & Data

What you’ll do Build across the stack. Design and ship internal tools and systems end to end — back-end services and APIs, front-end interfaces, data integrations, and the deployment and infrastructure glue that makes them real.

Work on many things. Move between projects and problem types as priorities shift — one week a workflow-automation tool, the next an internal dashboard, the next helping ship an AI-powered feature. Breadth is the point.

Put AI to work. Integrate the LLM, RAG, and agent capabilities the team builds into usable software, partnering closely with the Applied AI Engineer to turn intelligence into product.

Enable the company. Sit with teams across the business, understand their problems, and build the right solution — measured by how much more effective you make everyone else.

Ship reliably. Own what you build through to production and beyond, with the quality and judgment to know when good-enough-shipped beats perfect-delayed.

What you'll bring: Strong full-stack engineering. You write production-grade code and build comfortably across the stack — back end, APIs, and front end (e.g. Python and/or TypeScript with a modern web framework). You’re not boxed into one layer.

Range and adaptability. A track record of picking up unfamiliar problems and shipping — you’re energized by variety, not thrown by it.

Comfort with AI as a tool. You’ve worked extensively with AI — integrating APIs, building features on top of models — even if AI isn’t your specialty.

A builder who talks to people. You can understand a non-technical team’s problem and turn it into a working solution. Internal enablement rewards engineers who listen as well as they build.

Data fluency. Enough comfort with pipelines, databases, and structured/unstructured data to work directly with what your software touches.

Autonomy in a small team. You thrive without heavy process, set your own direction, and are happy wearing whatever hat the moment calls for.

Bonus points Experience building internal tools or platforms that other teams depend on.

Cloud, DevOps, or deployment experience — CI/CD, containers, infrastructure-as-code.

Hands-on experience shipping LLM-powered features in production.

A portfolio of varied systems you’ve built end to end.