Lead Technical Product Manager, Wolters Kluwer

• 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

Desirable

• 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
• Backlog Ownership & Agile Execution (30%)
• 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
• Agentic AI Product Development (25%)
• 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
• Lean Innovation & Experimentation (25%)
• 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
• Discovery & Cross-Functional Collaboration (10%)
• 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
• Metrics and Reporting (10%)
• 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
• What Success Looks Like
• A transparent, prioritized backlog with 2–3 sprints of ready stories and minimal rework
• Shipped agentic capabilities for the October 2026 1040 Prep GA that meet acceptance criteria for task completion, intervention rate, decision accuracy, safety, and usability
• Faster learning cycles, fewer blocked items, and clear evidence that shipped work solves real user problems
• Short, useful updates that keep stakeholders aligned without ceremony overhead
• Consistent delivery with decreasing cycle times and increasing customer value