Senior Product Manager - Internal AI

Company

Brightly Software, Inc.

Mid-level Professional

Job type

Full-time

Remote only

Employment type

Permanent

Location(s)

• Raleigh
  - North Carolina
  - United States of America

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.

• 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 itwarrantsinvestment of engineering capacity
• Mapping and redesigning workflows toidentifywhere 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 andbeing responsible forthe follow-up when tools fall short of adoption targets, not handing the diagnosis off
• Leading organizationally across SI AMS, not just delivering toit. That means influencing how senior leaders think about AI investment, helping business groups build AI readiness, and shaping how the function matures over time

• 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…