Senior Applied AI Engineer

Hologic•San Diego, CA

4d•$100,000 - $130,000•Hybrid

About The Position

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.

Requirements

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/TypeScript, 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 authentication. We run our AI solutions on our own Azure infrastructure, so you will be working in it from day one.
Experience leading requirements conversations with business stakeholders and defining success measures with them.
A track record of owning projects end to end and getting them into production with limited direction.
Clear communication with both technical and business audiences, including senior leaders.

Nice To Haves

Experience applying AI or analytics in Quality (quality systems, complaint handling, CAPA, audits) or Commercial (sales, marketing, pricing, customer analytics) settings.
Experience shipping AI agents, RAG applications, or copilots to production.
Familiarity with orchestration frameworks such as LangGraph or Semantic Kernel, or with Model Context Protocol (MCP).
Experience with Power Platform or similar workflow automation tools.
Experience with containerization, monitoring, observability, and production support.
Background in business analysis, consulting, or product management.
Experience working in a regulated environment.

Responsibilities

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/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.
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.