Senior AI Product Manager

Position Summary The Senior AI Product Manager (AI PM) will own the strategy, roadmap, and delivery of AI-powered products and capabilities that improve operational efficiency, user experience, and decision-making across the business. This role is responsible for identifying high-value AI opportunities, translating them into clear product requirements, aligning cross-functional stakeholders, and partnering closely with Engineering, Data/ML, Security, and Operations to deliver AI solutions that are scalable, trustworthy, and measurable. This role will support both internal and resident‑facing AI initiatives; scope will evolve based on business priorities and validated use cases.   Essential Duties AI Product Strategy & Roadmap Define and own the vision, roadmap, and success metrics for AI products (e.g., workflow automation, predictive insights, document intelligence, conversational/agentic experiences). Identify and prioritize AI use cases that deliver measurable business outcomes (cost reduction, cycle-time improvements, risk reduction, revenue lift, employee productivity). Conduct discovery with business stakeholders and end users to validate pain points and value hypotheses.   Product Execution & Delivery Translate validated AI opportunities into well‑defined product requirements and a prioritized delivery backlog, with clear acceptance criteria covering quality, safety, and performance. Partner with delivery teams to plan and execute work using sprint‑based or flow‑based (Kanban) approaches, ensuring predictable delivery and business readiness. Ensure successful business adoption by partnering with end users, training teams, and operational leaders to drive measurable outcomes post-launch. Ensure the product is “operationally real,” including training, rollout planning, support readiness, and documentation.   AI/ML Lifecycle, Governance & Trust Define product performance metrics such as accuracy, latency, cost-to-serve, user adoption, retention, and task success rate. Establish ongoing monitoring and feedback mechanisms to ensure product reliability and effectiveness, including triggers for updates and improvements in collaboration with Engineering. Ensure responsible practices by implementing privacy, security, fairness checks where appropriate, maintaining explainability standards, and supporting auditability. Work closely with Security and Compliance teams to guarantee data handling, permissions, and governance adhere to enterprise requirements.   Stakeholder Alignment & Communication Serve as the cross-functional “translator” between business outcomes and technical implementation. Communicate roadmap progress, risks, tradeoffs, and decisions clearly to executives and operational leaders. Build alignment across teams and drive decisions in ambiguous problem spaces. Influences broader AI strategy and contributes to long-term platform and capability planning across the organization.   Vendor / Platform Partnership (as applicable) Evaluate and manage third-party AI vendors/tools (LLM providers, OCR/doc intelligence, MLOps platforms). Support procurement, contract negotiation inputs, and performance management against KPIs/SLA expectations.   Additional Duties: Tasks or duties not outlined in this job description may be required to contribute to the organization's success and efficiency.     Qualifications Education & Experience: 5+ years in Product Management (or equivalent), with at least 2+ years delivering AI/ML or GenAI-enabled products into production. Demonstrated experience taking products from discovery to launch, with measurable outcomes. Strong analytical skills and experience defining/using metrics to drive roadmap prioritization. Strong stakeholder management and executive communication skills. Bachelor’s degree required (STEM preferred) or equivalent experience.   Preferred Qualifications Experience with LLM/GenAI product patterns (RAG, tool/function calling, agent workflows), experimentation, and evaluation. Familiarity with MLOps and production AI considerations (monitoring, drift, retraining, safety guardrails). Experience with data governance concepts, integration patterns, APIs, and enterprise platforms. Experience in real estate, property management, finance/ops workflows, or regulated environments.