Senior Product Manager, AI

Buildout

21h•$150,000 - $170,000•Remote

About The Position

Buildout is hiring a Senior Product Manager, AI to own the strategy and roadmap for its AI assistant, which is embedded across the company’s commercial real estate brokerage software suite. The role sets which AI skills to build and when, determines when to use AI reasoning versus deterministic platform calculations, and establishes the quality bar for releases. It also brings AI into marketing and document-creation workflows that help brokers create materials for listings and get them to market and pitched faster. The Product Manager works day to day with Engineering and Design. The role is customer-facing and evidence-driven: it involves observing brokers’ workflows, testing AI skills against real use cases, translating findings into roadmap priorities and evaluation sets, and measuring both product outcomes and model quality. The position owns skills from concept through launch and iteration, including backlog prioritization, prompt and retrieval design, guardrails, specifications, and release quality reviews. This is a fully remote role open across most of the United States. Occasional travel may be requested for customer meetings, conferences, team offsites, or other business needs, typically a few times per year. Targeted compensation is $150,000–$170,000 plus bonus. The position reports to George Mountis, SVP, Product.

Requirements

At least 5 years of product management experience.
Experience shipping 1 to 2 AI/ML or LLM-powered features to real users.
Hands-on experience with rapid prototyping tools such as Claude, Cursor, or Replit.
Comfort with prompt and context engineering, retrieval-augmented generation (RAG), and a practical understanding of LLM capabilities and limitations.
Strong product discovery skills, including customer interviews, problem validation, and translating insights into roadmap priorities.
Experience partnering closely with Engineering and Design to ship high-quality software in a fast-paced environment.
Technical depth sufficient to discuss tradeoffs involving models, data, and latency with engineers.
Clear communication with technical and non-technical audiences, and comfort with ambiguity and probabilistic outcomes.
Ability to make evidence-based product decisions, define success upfront, and use customer feedback and data.
Ability to balance product strategy with hands-on execution and make thoughtful decisions amid ambiguity, tradeoffs, and evolving technology.

Nice To Haves

Experience in commercial real estate, proptech, or vertical SaaS.
Experience building assistants, copilots, or agentic products.

Responsibilities

Set the vision, strategy, and roadmap for AI, including which skills to build and in what order.
Spend time with customers, observe their workflows, test AI skills against live workflows, and apply findings to the roadmap and evaluation sets.
Prioritize the backlog based on broker value, technical feasibility, and quality risk, and manage work from concept through launch and iteration.
Define and maintain evaluation sets and quality standards for AI skills, and review regressions before releases.
Determine when deterministic logic is preferable to LLM-based reasoning for a skill.
Own prompt, context, and retrieval design, and improve it using measured evaluations.
Establish guardrails, including grounding in real records, permission scoping, confirmation before actions, hallucination controls, and rollback plans for non-deterministic failures.
Translate broker workflows into implementable skill specifications covering triggers, inputs, outputs, guardrails, and surfaces.
Ask questions and unblock Engineering.
Measure product outcomes such as adoption and task success, and model quality such as accuracy, groundedness, and regression rate; monitor latency and cost against quality.
Bring AI into marketing and document-creation workflows for commercial real estate brokers.