Senior Applied AI Engineer

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.

• 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 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/Type Script, 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.

• 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.

• Explain AI capabilities, limits, and risks clearly to technical and non-technical audiences alike.
• Document your designs and decisions so others can support and build on your work.
• Coach teammates, review designs, and contribute to shared patterns and standards as the team grows.

• Bachelor's degree in a technical field, or equivalent practical experience.
• Five to eight years in software, solutions, data, or ML engineering, including at least two years building AI, ML, or automation solutions.
• Solid programming skills in Python, JavaScript/Type Script, C#, or a similar language, plus a willingness to use low-code platforms when they're the better fit.
• Working knowledge of modern AI, including LLMs, RAG, agents, prompt engineering, and evaluation.
• A current view of the frontier model landscape — including Claude, OpenAI, and Gemini models and the leading open-source models — and a practical sense of what each is good at.
• Hands‑on experience building, integrating, and deploying applications on Azure, working with APIs, cloud services, and…