Senior Applied AI Engineer
Role Summary
We are hiring a Senior Applied AI Engineer to help us find and deliver practical AI solutions across the company. This is a hands-on role that covers the full life of a project — from sitting down with business teams to understand a problem, to deciding whether and how AI can help, to building the solution, getting it into production, and making sure people actually use it.
You will split your time between business-facing work and engineering. Some weeks that means running discovery sessions and mapping how a process works today; other weeks it means writing code or configuring a platform. We care more about solving the problem well than about which tool you used to solve it.
You should be comfortable starting from a vague problem rather than a written spec — asking good questions, defining what success looks like, and moving the work forward without waiting to be told what to do next.
What You Will Do
Work with the business
- Meet with business teams to find and prioritize problems where AI can genuinely help, and be honest about where it can't.
- Run discovery sessions: ask good questions, map how the work gets done today, and pin down the actual problem before proposing anything.
- Define what success looks like for each project, including the measures you will use to show it worked.
- Turn rough ideas into clear problem statements, options, and plans that both leaders and engineers can act on.
Design and build
- Design and build AI solutions such as copilots, agents, retrieval-augmented generation (RAG) applications, and workflow automations on the platforms we use today, including Azure OpenAI, Claude, and Microsoft Copilot.
- Pick the right approach for each problem, whether that's custom code (Python, JavaScript/TypeScript, C#) or configuration on platforms like Power Automate or Logic Apps.
- Follow solid engineering practices: version control, testing, CI/CD, and evaluation of AI output quality.
- Integrate what you build with our enterprise systems, data, and APIs, with security considered from the start.
- Stay with each project through deployment, adoption, support, and improvement. You own the outcome, not just the code.
Make it enterprise-ready
- Work with our security, architecture, governance, and compliance teams to get solutions ready for production.
- Weigh trade-offs like cost, performance, reliability, and maintainability, and explain them in plain business terms.
- Set up monitoring and feedback loops, track whether the solution is being used and delivering value, and adjust based on what you learn.
- Help teams adopt what you build through training, feedback sessions, and change support.
Share what you know
- Explain AI capabilities, limits, and risks clearly to technical and non-technical audiences alike.