AI Product Manager (USA)

About the role

Most AI companies are building tools. We're building the worker. Freehand's AI agents replace the human decision-making and coordination layer that sits above enterprise software — handling the analysis, negotiation, prioritization, and execution that previously required armies of logisticians / supply chain professionals. Our customers include Apple, Meta, GE, Pfizer, J&J, Schlumberger, Cardinal Health, and Unilever. They chose us because we have a fundamentally different answer to what enterprise software looks like when AI can do the job. If you want to work on something that matters, with people who will push you to be sharper than you thought you could be, this is the place.

As an AI Product Manager at Freehand‑ Teams for Business Spend Management, you’ll define and deliver the future of autonomous p2p workflows. You will partner with Engineering, Data Science, UX, and go‑to‑market teams to ship production‑grade AI agents that execute and optimize complex spend workflows with precision, reliability, and explainability. You’ll work directly with CXOs at Fortune 100 companies to understand pain points and convert them into product innovations that displace traditional outsourcing, staffing and SaaS models with intelligent, self‑evolving agents. This is a foundational leadership role with scope to define vision, architecture, and roadmap, and execute against it, for one of the most critical domains in enterprise automations, at the cutting edge of generative AI.

Responsibilities

• Own product vision, architecture, and execution for Freehand’s AI Teams for Spend Management across key use cases:
• Invoice validation & matching
• Vendor onboarding & collaboration
• Contract and compliance management
• Dispute resolution
• Cross‑border payments & remittance
• Define agent design patterns—multi‑capability prompting, tool orchestration, retrieval pipelines, fallback logic, and policy hierarchies—that balance accuracy, reliability, and real‑world business context.
• Ensure end‑to‑end observability, including agent health, performance monitoring, confidence scoring, and auditability of decisions.
• Partner with AI/ML and data engineering teams to build closed‑loop training pipelines and eval sets that drive continual improvement via human‑in‑the‑loop feedback, signal instrumentation, and structured reasoning.
• Drive high‑fidelity UX for AI interactions, enabling explainable, proactive, and intuitive agent experiences across interfaces (UI, email, chat, APIs).
• Lead technical conversations with enterprise architects and strategic conversations with CFOs, CIOs, and Chief