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.