Lead Technical Product Manager, Wolters Kluwer

• This position requires deep technical understanding of generative AI combined with exceptional agile product ownership skills to drive rapid iteration and continuous customer feedback cycles
• 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 features in production strongly preferred
• Experience with lean product development and build-measure-learn methodologies
• Demonstrated experience in startup environments or innovation labs 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 generative AI concepts including LLMs, RAG architectures, prompt engineering, embeddings, and vector databases
• Working knowledge of Azure OpenAI Service, prompt patterns, evaluation approaches, and safe response behavior
• Strong grasp of INVEST principles and story mapping techniques
• Understanding of API integrations and microservices architectures
• Knowledge of AI evaluation metrics, testing strategies, and MLOps practices
• Understanding of data privacy, security, responsible AI, and auditability 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 – Lead Technical Product Manager – Generative AI is an impactful individual contributor who transforms strategic AI initiatives and product vision into executable backlog items the team can deliver
• This role bridges product strategy and agile product ownership, development, and execution of the tactical delivery of generative AI capabilities through disciplined backlog management and agile practices across multiple TAA modules
• 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
• 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
• We partner with product and engineering teams to bring responsible Generative AI into real workflows, grounded in authoritative content and built on the Microsoft Azure ecosystem
• Our approach emphasizes customer obsession, build-measure-learn iterations, and fast value delivery to transform how professionals work
• Backlog Ownership & Agile Execution (30%):
• Lead the integrated plan for work that spans multiple modules; align product, engineering, and UX to support rapid GTM
• Transform epics into clear, INVEST features and user stories (Independent, Negotiable, Valuable, Estimable, Small, Testable) 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
• Generative AI Product Development (25%):
• Specify product requirements for Azure OpenAI-based features, including grounding to authoritative sources, citation behavior, refusal/abstain rules, and graceful error handling
• Understand customer workflows and jobs-to-be-done to effectively decompose AI-driven solutions into implementable features; identify where automation/AI can deliver value within existing user journeys
• Collaborate on RAG requirements: content sources, chunking strategy, embedding selection, vector search, retrieval approach, and evaluation criteria
• Define AI-specific acceptance criteria and SLOs: groundedness/relevancy, quality thresholds, latency budgets (sub-3s), concurrency, and cost per interaction
• Coordinate prompt templates, model change control, and safety guardrails so demos, pilots, and production remain predictable
• Work with engineering to define fallback strategies and error handling for AI features
• Establish evaluation metrics including performance benchmarks (latency, accuracy, groundedness)
• 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 needs and solution viability
• Define problem-solution fit and product-market fit 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
• Apply lean startup principles to reduce waste and accelerated 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 flows that feel intuitive and require minimal training
• Work horizontally with platform, security, compliance, and content teams to meet privacy, safety, and auditability 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, AI quality and latency
• Tie backlog items to measurable outcomes and close the loop with post-release verification
• Track and report on key AI metrics including model performance, user adoption, and business impact
• What Success Looks Like:
• A transparent, prioritized backlog with 2-3 sprints of ready stories and minimal rework
• Shipped GenAI capabilities that meet acceptance criteria for grounding, safety, latency, 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