AI Product Management Director - Realpage

Overview

We are seeking a hands‑on Product Director, AI/ML to lead strategy and execution for high‑impact AI/ML capabilities across the company. This role serves as the shared‑services AI/ML product lead, enabling commercial and internal product features with scalable, reusable, and compliant AI/ML capabilities. The roles begins as an Individual Contributor Director reporting directly to the VP, Product Management for the Lumina Data Platform, with the opportunity to grow into a people‑leadership role as our AI/ML requirements expand. Additionally, this role has end‑to‑end product ownership for LLM capabilities within the Data & Analytics business unit—driving autonomous workflows, multi‑step reasoning agents, orchestrated task flows, and high‑value data‑intensive AI capabilities. This is a high‑visibility role ideal for a product leader who combines strong technical fluency, cross‑functional leadership, and drives execution across Product, Data Science and Engineering.

Responsibilities

• Lead shared‑services AI/ML product capabilities used across multiple product lines.
• Partner with platform, data science, engineering, and BU product leadership to align on AI/ML vision, drive roadmap, execution and capacity management.
• Oversee roadmap and delivery for Agentic AI within the Data & Analytics business unit.
• Influence long‑term organizational design and may take on people leadership as the function scales.
• Balance commercial and internal product priorities, maintaining an investment strategy aligned to enterprise and BU strategic goals
• Own product strategy and delivery for AI governance and ML Ops, establishing robust frameworks for model lifecycle management, compliance, and operational excellence.

KEY RESPONSIBILITIES

1. AI/ML Product Strategy & Vision

Own the AI/ML capability roadmap and vision aligned to portfolio strategy.

Define multi‑quarter investment themes that balance internal acceleration with commercial product differentiation.

Identify opportunities where LLMs can accelerate classical ML cycles, including automated evaluation, data summarization, hypothesis generation, and model quality refinement.

2. Shared‑Services AI/ML Ownership

Serve as the central product lead for shared AI/ML components, including vector stores, RAG pipelines, evaluation harnesses, LLM safety tooling, feature stores, and model governance frameworks.

Drive adoption across multiple product lines, ensuring consistency, compliance, and time‑to‑value acceleration.

Reduce duplication and enable data science to build AI/ML capabilities faster and more safely.

3. LLM Ownership (Data & Analytics BU)

Own the full product strategy and delivery of LLM capabilities within the D&A business unit.

Define value propositions for autonomous