Senior Technical Product Manager - AI

About Us:

insightsoftware is a global provider of reporting, analytics, and performance management solutions that unlock the potential of business data and transform the way finance and data teams operate. We empower leaders from over 32,000 organizations to make timely and intelligent decisions. Our comprehensive solutions span Financial Planning and Analysis (FP&A), Controllership, and Data and Analytics. We deliver finance teams the insights required to navigate any economic climate and drive greater financial intelligence, while increasing productivity, visibility, accuracy, and compliance. Learn more at insightsoftware.com.

Job Description:

We are seeking a Senior Technical Product Manager to own the product strategy and execution for the AI Services team at insightsoftware. This is a dedicated product role for a team that already builds the agentic capabilities, tool and connector layer, and AI quality infrastructure that the entire insightsoftware portfolio depends on. You will take the roadmap from a shared responsibility across engineering leadership and make it your own — setting direction, sequencing the work, and driving it to delivery.

AI Services is one of our most visible and strategically important programs. It operates as a shared service, which means our internal customers are the product teams across the insightsoftware portfolio, and our end customers are the finance and accounting professionals who use their products every day. This role is a senior individual contributor position: you will work as a peer to our chief architect and engineering manager, go deep on technical design decisions, and write the specifications, evaluation criteria, and enablement material yourself. Success requires someone who has built agentic systems in production, not someone who has only read about them.

What Will You Do?

• Own the AI Services Roadmap: Define and maintain the strategy, roadmap, and quarterly priorities for agentic workflows, the tool and connector layer, and AI quality infrastructure. Make the trade-off calls on sequencing, scope, and technical direction, and defend them with evidence.

• Define Agentic Capabilities in Depth: Specify multi-step agent behavior — planning, tool selection, memory, human-in-the-loop checkpoints, fallback paths, and guardrails. Translate ambiguous business problems into agent designs that are reliable enough to put in front of finance users.

• Drive the Tool and Connector Strategy: Own the roadmap for our Model Context Protocol (MCP) surface and tool interfaces. Decide which insightsoftware data, actions, and product capabilities are exposed to agents and third-party assistants, and define the contracts, versioning, and access controls that make them safe to consume.

• Establish AI Quality as a Product Discipline: Build and own the evaluation framework for AI features — golden datasets, offline and online evals, regression harnesses, accuracy and hallucination measurement, and production observability. Set the quality bars that gate release and hold the team to them.

• Enable Consuming Product Teams: Treat portfolio product managers and their engineering teams as your customers. Run intake, document integration paths, publish reference implementations and adoption guidance, and measure whether teams are successfully building on AI Services rather than around it.

• Partner Deeply with Engineering and Architecture: Work daily with the chief architect and engineering manager on design reviews, technical trade-offs, model and vendor selection, and cost and latency management. Participate in sprint planning and refinement, and unblock the team on product decisions in hours, not weeks.

• Measure and Communicate Outcomes: Define the metrics that show whether AI Services is working — adoption across product lines, agent task success rates, eval scores, cost per task, latency, and incident rates. Report them to senior leadership with a clear read on what to do next.

• Manage AI Risk and Trust: Work with security, legal, and compliance to address data handling, model governance, auditability, and customer trust concerns before they become launch blockers.

Qualifications

Minimum Qualifications:

• 8 or more years in product management, with at least two years owning AI products as a hands-on individual contributor

• Demonstrated experience shipping large language model applications to production — not prototyp…