Senior Product Manager - Internal AI

Siemens•Raleigh, NC

2d•$102,459 - $175,644•Remote

About The Position

At Siemens, we help organizations transform maintenance and operations through connected insights, AI-powered technology, and intelligent asset management solutions. Our software enables customers to manage the full lifecycle of assets, facilities, and infrastructure while improving efficiency, reducing risk, and optimizing long-term investments. By connecting data, people, and processes, we empower organizations to make smarter decisions, maximize asset performance, and achieve more resilient operations. We are seeking a Senior Product Manager to shape how AI and automation improve the way our Siemens Asset Management Software organization operates. In this highly visible, internal-facing role, you will partner across the business and with AI Engineering to turn complex opportunities into practical solutions, measurable outcomes, and scalable ways of working. This Senior Product Manager owns the product function for the SI AMS internal AI team. The clients are not external customers. They are SI AMS employees across R&D, Sales, Customer Success, Finance, Operations, and every other group in the business. The role is to identify where AI and automation can improve how SI AMS operates, build a prioritized roadmap for the AI Engineering team, and ensure that every solution delivered drives measurable outcomes for the groups it was designed to support. The Senior Product Manager works directly with the SI AMS AI Engineering team, including engineers, architects, and an AI Product Owner. The role reports to the Head of Product Ownership and Product Enablement and sits at the center of two functions: business problem discovery and AI delivery. This is a build-from-zero role. There is no existing discovery function, intake process, or roadmap to inherit. The successful candidate will build those capabilities from the ground up, with active organizational support and leadership engagement from day one. This is the right opportunity for someone who wants real ownership of the discovery function, the roadmap, and the outcomes.

Requirements

8+ years in product management, technical product management, product ownership, business-process transformation, or a closely related role, with end-to-end ownership of a software, data, or AI-enabled product from discovery through adoption
Recent hands-on experience delivering at least one AI/ML, generative AI, intelligent automation, or advanced analytics initiative in a production or operational environment
Working fluency in LLM applications, prompt engineering, retrieval-augmented generation, agentic systems, and AI evaluation, enough to challenge assumptions, assess feasibility, and prototype independently using tools like Claude Code, Copilot Studio, Azure AI Foundry, n8n, or Power Automate
Experience discovering and translating ambiguous internal user needs, where the requester often does not know what they actually need, and turning them into scoped problem statements with clear success criteria
Ability to map and redesign business workflows, decisions, dependencies, exceptions, controls, and handoffs, and define the baselines, critical metrics, and instrumentation needed to know whether a solution actually worked
Demonstrated ability to lead cross-functional initiatives at a senior level, building alignment across business and technical stakeholders, driving decisions, and owning outcomes without formal authority
Extreme ownership and relentless execution, taking unclear problems from vague to actionable, delivering on commitments without being directed, and owning outcomes, including when results miss the mark
Customer-centric focus and steadfast curiosity about how technology can change how organizations work. You surface problems ahead of formal requests, challenge assumptions, and keep pushing to understand what AI can realistically do
Clear and direct communication across Engineering, business teams, and leadership, with the ability to influence priorities, navigate competing viewpoints, and resolve trade-offs without formal authority
A bachelor’s degree in computer science, engineering, information systems, data science, business, or a related subject area, or equivalent practical experience

Nice To Haves

Experience in an internal-facing product role, where customers are colleagues and influence depends entirely on the trust you build
Resourceful problem-solving when there is no playbook, moving forward through unclear requirements, shifting constraints, and no established template
Grit and composure under fire, deciding with incomplete information, staying steady when priorities shift, and holding accountability when outcomes miss
Empathy and infectious enthusiasm, building real relationships, bringing energy that moves hard problems forward, and making the Engineering team want to work on what you bring
Hands-on experience with workflow automation, iPaaS, low-code tools, or event-driven integration, and a clear sense of where automation ends and AI begins
Understanding of responsible AI concepts and safeguards, including data privacy, bias mitigation, explainability, prompt injection protection, human oversight, and enterprise governance

Responsibilities

Building and running the internal discovery function, with structured relationships across SI AMS to surface, validate, and prioritize automation opportunities before they become requests
Writing clear problem statements for each initiative, with defined baselines and success criteria, before any engineering work begins
Assessing AI feasibility, whether a problem is technically tractable, whether the necessary data exists, and whether it warrants investment of engineering capacity
Mapping and redesigning workflows to identify where AI, automation, and human judgment should each apply, including decision rights, exception handling, and escalation paths
Owning the roadmap, sequencing work against business outcomes and strategic alignment, and making the call when groups compete for the team’s time
Using hands-on experimentation as a core method, building prototypes, running prompt and context engineering tests, and generating evidence about what to build, revise, or stop
Tracking outcomes after launch and being responsible for the follow-up when tools fall short of adoption targets, not handing the diagnosis off
Leading organizationally across SI AMS, not just delivering to it. That means influencing how senior leaders think about AI investment, helping business groups build AI readiness, and shaping how the function matures over time