Lead Technical Product Manager – Agentic AI

Wolters Kluwer•Riverwoods, IL

About The Position

Lead Technical Product Manager – Agentic AI is an impactful individual contributor who transforms strategic agentic AI initiatives and product vision into executable backlog items the team can deliver. This role bridges product strategy and tactical delivery, owning agile execution of autonomous, multi-step AI workflows that prepare tax returns and complete complex professional tasks end-to-end. Reporting to the Director of Innovation, you will partner daily with Product Managers, Engineers, and UX to decompose epics into features and INVEST-compliant user stories, ensuring development teams have clear, prioritized work that delivers customer value incrementally. This position requires deep technical understanding of agentic AI systems — including planning, tool use, and human-in-the-loop orchestration — combined with exceptional agile product ownership skills to drive rapid iteration and continuous customer feedback cycles. You will advise management on release readiness and risk and bring the voice of the customer into the team to ship outcomes that solve real problems for tax and accounting professionals. About InnovateHub & Agentic Tax InnovateHub operates as Wolters Kluwer's internal innovation accelerator within TAA North America Professional Business Unit, functioning like a startup across the division. We co-design with customers, run lean experiments, and ship high-value capabilities quickly through rapid validation cycles. Our approach emphasizes customer obsession, build-measure-learn iterations, and fast value delivery to transform how tax and accounting professionals work.

Requirements

Bachelor's degree from an accredited university in Computer Science, Engineering, Business, or related field, or equivalent experience
5–7+ years in software product management or product ownership in B2B SaaS environments
4+ years practicing Agile/Scrum in Product Owner or Lead PM capacity, working closely with engineering
2+ years working with AI/ML products, with hands-on experience shipping Generative AI or agentic features in production strongly preferred
Experience with lean product development and build-measure-learn methodologies
Demonstrated experience in startup environments or innovation labs preferred
Tax, accounting, or professional services software domain experience strongly preferred
Expert backlog hygiene in Azure DevOps Boards: epics to features to stories, acceptance criteria, Definition of Ready/Done, dependency tracking, release planning
Deep understanding of agentic AI concepts including LLM-based planning, tool/function calling, multi-step orchestration, state management, and human-in-the-loop design
Working knowledge of Azure OpenAI Service, agent frameworks, prompt patterns, evaluation approaches, and safe response behavior
Familiarity with Microsoft Copilot, declarative agents, and the M365 ecosystem
Strong grasp of INVEST principles and story mapping techniques
Understanding of API integrations, tool/function specifications, and microservices architectures
Knowledge of AI evaluation metrics for agentic systems (task success, intervention rate, decision accuracy), testing strategies, and MLOps practices
Understanding of data privacy, security, responsible AI, auditability, and §7216 compliance in enterprise environments
Problem-first, customer-obsessed, and evidence-driven mindset
Self-starter mentality with ability to work independently in ambiguous environments
Critical thinking skills to challenge assumptions, simplify complex requirements, and validate hypotheses
Exceptional written and verbal communication for technical and non-technical audiences
Comfort with rapid iteration and ability to pivot based on learning
Strong facilitation and conflict resolution skills
Clear, direct communicator who collaborates well across functions

Nice To Haves

Certified Scrum Product Owner (CSPO/PSPO) or SAFe POPM certification
Azure AI-900 or AI-102 certification
Background in tax preparation, accounting, or professional services software
Experience with Microsoft Copilot Studio or declarative agent development
Experience with agent frameworks (LangGraph, AutoGen, Semantic Kernel, or similar)
Experience managing distributed or remote development teams
Familiarity with document intelligence technologies

Responsibilities

Lead the integrated plan for work that spans multiple modules and agentic workflow components; align product, engineering, and UX to support rapid GTM
Transform epics into clear, INVEST features and user stories with precise acceptance criteria and Definition of Ready/Done
Ensure voice of customer and market data flows into sprint planning and backlog prioritization; translate customer feedback into actionable user stories
Maintain a prioritized backlog in Azure DevOps Boards with 2–3 sprints of refined, ready work, visible dependencies, and unblocked paths to delivery
Apply lightweight prioritization methods (value, risk, effort, sequencing, cost of delay) with documented rationale
Lead backlog refinement sessions, sprint planning, and story elaboration with development teams
Partner with Engineering on slicing, technical feasibility, release planning, feature flags, and canary rollouts
Collaborate with Scrum Master to optimize team flow metrics, maintain predictable delivery, and remove impediments
Apply eXtreme Programming (XP) practices where appropriate, including test-driven development support
Specify product requirements for autonomous, multi-step agent workflows, including planning behavior, tool selection, action sequencing, and human-in-the-loop checkpoints
Understand tax preparation workflows and jobs-to-be-done deeply enough to decompose them into agent tasks; identify where autonomous execution delivers value vs. where human review is required
Define agent capabilities and constraints: which tools agents can call, what actions require user confirmation, and how state is managed across multi-step interactions
Collaborate on retrieval and grounding requirements where agents draw on authoritative tax content (IRS publications, prior-year returns, client documents)
Define agent-specific acceptance criteria and SLOs: task completion rate, decision accuracy at branch points, intervention rate, recovery from failure, latency budgets, and cost per workflow
Coordinate prompts, agent instructions, model change control, and safety guardrails so demos, pilots, and production remain predictable
Specify integration requirements for Microsoft 365 and Copilot environments, including declarative agent definitions for the Agent Store
Work with engineering to define fallback strategies, error handling, and graceful degradation when agents encounter ambiguity
Run short build-measure-learn loops with focus on validated outcomes, not output volume
Design and execute rapid validation experiments to test hypotheses about user trust in autonomous workflows and where human oversight is essential
Define problem-solution fit and product-market fit for agentic capabilities that maximize learning with minimal development effort
Convert discovery signals and pilot feedback into backlog updates quickly; retire low-value items and reduce WIP
Track innovation metrics including time-to-validation, experiment velocity, and learning rate
Support A/B testing and feature flagging strategies for controlled rollouts of autonomous behaviors
Apply lean startup principles to reduce waste and accelerate validated learning
Coordinate with Product team for customer sessions; capture technical requirements and implementation considerations from these discussions
Coordinate with GTM lead to ensure engineering deliverables align with launch requirements; facilitate knowledge transfer to Sales, Support, and other internal teams pre-release
Support Product Managers in discovery by turning problem insights into hypotheses and testable stories
Integrate user feedback, analytics, and support signals into prioritization; ensure each story anchors to real user problems
Partner with UX on agent interaction patterns, transparency, and intervention flows that build user trust
Work horizontally with platform, security, compliance, and content teams to meet privacy, safety, auditability, and §7216 expectations
Produce concise artifacts that reduce ambiguity: story maps, acceptance test outlines, release notes, known limitations
Keep stakeholders aligned with short, factual updates: current focus, what shipped, what we learned, what's next
Partner with Scrum Master to maintain dashboards for delivery and product health: throughput, cycle time, story readiness, escaped defects, agent task completion rates, decision accuracy, and intervention frequency
Tie backlog items to measurable outcomes and close the loop with post-release verification
Track and report on key agentic AI metrics including workflow completion rates, user trust signals, model performance, and business impact