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 prototypes or internal demos 

• Working technical fluency in agentic architectures: orchestration and planning patterns, tool and function calling, retrieval-augmented generation, context management, and prompt engineering 

• Hands-on experience with the Model Context Protocol or comparable tool and plugin interfaces for connecting agents to enterprise systems 

• Practical experience building or operating evaluation frameworks for AI systems, including how to construct datasets, choose metrics, and interpret results 

• Experience delivering a shared platform or API-based service where other product teams are the primary consumers 

• Ability to read code, reason about system design, and hold your own in architecture discussions with senior engineers 

• Strong command of Agile delivery, with the judgment to know when process helps and when it gets in the way 

• Ability to influence cross-functional teams and senior stakeholders without formal authority 

• Excellent written communication — this role produces specifications, decision records, and enablement documentation that others rely on 

• Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience 

Preferred Qualifications: 

• Experience taking AI capabilities to market in a B2B enterprise SaaS environment, including packaging, pricing input, and sales enablement 

• Background in enterprise finance, accounting, ERP, or business intelligence domains 

• Experience managing inference cost, latency, and model routing across multiple providers at scale 

• Familiarity with AI security, data residency, and regulatory considerations for enterprise customers 

• Prior experience as the first product hire on a team, establishing product practice where none existed 

• Contributions to open source AI tooling, published evaluation work, or an active public presence in the agentic AI community 

Why This Role Matters 

Every insightsoftware product line is building toward AI-assisted workflows for the Office of the CFO. AI Services decides how fast and how well that happens. You will set the standard for what good looks like — and the leverage is unusual: one well-designed capability here reaches customers through many products at once.