Senior Technical Product Manager

Sr. Technical Product/Program Manager - AI Enablement

Requirements:

• 5+ years of combined enterprise level Product Management and Project/Program Management experience
• Proven experience managing delivery for internal enterprise platforms or tools
• Strong backlog management skills within a defined roadmap (execution‑focused, not greenfield)
• Experience working with AI/ML‑enabled products, data platforms, or analytics tools
• Ability to partner effectively with Enterprise Architecture, engineering, and technical stakeholders
• Hands‑on experience with Agile delivery, sprint ceremonies, and dependency management
• Strong communication skills with the ability to keep large, matrixed teams aligned and on schedule
• Comfort operating in fast‑paced, enterprise environments with multiple stakeholders
• Experience with reporting and executive level communication

Day to Day:

An enterprise architecture team is looking for a hybrid Technical Product Manager / Program Manager to drive execution for a new internal tool. They will keep the defined roadmap on track while enabling continuous improvement of an AI‑enabled internal platform. This role is 60–75% project/program management and 25–40% product management, focused on backlog management, cross‑team coordination, and ensuring ongoing product value. The goal of this platform is to have an LLM "Ask" feature, and additional tooling that can help with SDLC capability and requirement definition, HLSD building, and identify dataflows through internal systems.

Responsibilities:

• Own delivery execution for the internal architecture product and related tools, ensuring roadmap milestones stay on track
• Manage and groom the product backlog, translating architectural and enterprise needs into clear epics, features, and user stories
• Partner with Enterprise Architects, data/AI teams, and engineering to coordinate dependencies and delivery timelines
• Track progress, risks, and blockers across workstreams; proactively resolve delivery issues
• Drive sprint planning, status reporting, and release coordination across stakeholders
• Ensure alignment between AI capabilities, enterprise architecture standards, and internal user needs
• Maintain documentation, delivery cadence, and operational discipline for a complex internal tool
• Support adoption and usability feedback loops while protecting roadmap stability