AI-Enabled Product Manager

The role involves leading the delivery of custom data and AI platforms for institutional real estate owners, operators, and fund managers. These are fixed-fee, fixed-date engagements of roughly sixteen weeks, with payment tied to signed gates, and you serve as the day-to-day lead.

A typical cycle integrates every system the client operates—property accounting, third-party operator packages, investor and fund platforms, deal and leasing platforms, loan and JV documents, models in Excel and SharePoint—into a single trusted asset record hosted in their cloud environment, read-only and never writing back to the source. On top of this, AI agents map new operator data, run data checks, abstract loan and JV terms, answer portfolio questions with sources cited, and draft quarterly commentary, feeding the views each group needs: portfolio and asset performance, lease and tenant exposure, debt and covenants, equity and JV structure, business plan against locked underwriting, quarterly reporting, and an exceptions log. Often, the cycle also involves retiring an incumbent platform by a contract date.

You own the plan, gates, and scope line, supported by a data and technology lead, a full-stack engineer, a solution architect, and a partner on business outcomes.

Key Responsibilities:

• Own the cycle end-to-end. Lead the full product development lifecycle across discovery workshops, data mapping, platform build, views, training, and handover, against a fixed fee and a firm finish date.
• Run gate-based delivery and hold the scope line. Define gate criteria, evidence, and acceptance with the client before signature. Translate an ambitious scope into what one cycle can deliver, document what moves to later cycles, and manage change so a fixed fee stays fixed. Set a value baseline with client finance at the start and measure against it.
• Put the data foundation first. Treat numbers that tie as the adoption bar. Work with the data lead on source mapping, data quality rules, exception routing to the responsible deal team, lineage, and locked snapshots before any view is built.
• Define the views with the people who use them. Run workshops with asset management, finance, investor relations, deal teams, and technology, and turn what they say into view specs, KPI definitions, and acceptance criteria the client signs.
• Decide where AI belongs, and where it does not. Work with engineering and data science on agent-based mapping, document extraction, retrieval with cited sources, and drafted commentary, while keeping reported financial figures on fixed, auditable calculations the client approves.
• Own the client relationship at a working level. Be the primary contact for the executive sponsor, the business owner, and the technology lead. Report progress plainly and surface problems before they land.
• Land the retirement and the handover. Plan the history migration, the parallel run, and the sign-off that the old system can be switched off. Train teams by role inside the cycle and transfer documentation and operating knowledge to the client's platform owner so their team builds what comes next.