Head of Product, AI Workforce

DescriptionBuild the Future WorkforceWand turns AI into labor. It enables humans and AI agents to operate together as a unified, hybrid workforce, with comprehensive management and oversight. And it’s already operating at scale inside some of the world’s largest organizations.Wand built the world’s first Agentic Labor Infrastructure enabling governments and global enterprises to create, manage, and scale digital workforces.Our mission is to integrate agent ecosystems into the core of work and business, unlocking a generational leap in the global economy. We’re building the infrastructure that lets humans and AI agents operate together safely, transparently, and at scale.Join Wand in leading the Agentic ShiftWand is building a high-performing global team who take full ownership of what they build. We lead by example, move fast, make data-aware decisions, and continuously push for more- always with a focus on delivering real value to customers.You would be joining a world-class team that combines deep research expertise and real-world product execution, with experience spanning Deepmind, Google, Amazon, Miro, Elise AI, IBM and Accern.RequirementsPosition SummaryYou'll lead the product team defining what an "agent" actually is. This means owning the core AI workforce product: execution, process automation, communication, and skills/training. This role combines hands-on product development with thought leadership on agentic AI while shipping to enterprises with 50k+ employees.You'll own the AI Workforce product stream including agent execution, workflows, communication, and skills & university (training/maturation). You'll define product strategy and roadmap for the core agentic system, establish the definition of "what is an agent" for the company and industry, and lead Skills & University where we train models, optimize prompts, and mature agents with organizational context. You should be able to explain to the CEO and CTPO what an agent really means.ResponsibilitiesLead product for 4 teams: execution, process automation, communication, skills & universitySpend significant time in skills & university, the core innovation area for agent training and maturationDefine agent architecture: Is a process an agent? Is an agent a human replacement? These are nuanced questions you'll answer.Ship features that work for enterprises, not just demos for engineersWork closely with your engineering counterpart to balance innovation and executionParticipate in EPD leadership and shape overall product directionBuild credibility as someone who can write or speak authoritatively on agentic AIShip agent training and maturation system (skills & university) to production in first 12 monthsDefine and document "what is an agent" framework adopted across the companyLead 3+ enterprise customer implementations and iterate product based on feedbackPublish or present thought leadership on agentic AI externallyReduce ambiguity in agent architecture so engineers know what they're buildingKey QualificationsDirector-level Product Manager or Group Product ManagerHands-on experience building agentic AI systems (non-negotiable)ML/AI product background required with substantial depth in the fieldExperience with LLM training, prompt optimization, or agent frameworks (n8n, CrewAI, Sierra, etc.)Enterprise product sense: you understand org processes, compliance, separation of concernsAbility to go deep technically and explain agent architecture to engineersComing from product organizations, not engineering or researchPreferred ExperienceBuilt vertical AI products serving enterprisesWorked on agentic systems that went to production, not just prototypesExperience defining "what is an agent" at both the conceptual and implementation levelsBackground in skills training systems or model maturation platformsPersonal CharacteristicsThought leader mindset with ability to influence industry directionHands-on approach, not just strategic oversightDeep technical curiosity about agent architecture and capabilitiesStrong communication skills for explaining complex conceptsComfortable with ambiguity and defining new categoriesBias toward shipping and learning from production deploymentsOriginally posted on Himalayas