Head of Product, AI Workforce

Description

Build the Future Workforce

Wand 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 Shift

Wand 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.

Requirements

Position Summary

You'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.

Responsibilities

• Lead product for 4 teams: execution, process automation, communication, skills & university
• Spend significant time in skills & university, the core innovation area for agent training and maturation
• Define 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 engineers
• Work closely with your engineering counterpart to balance innovation and execution
• Participate in EPD leadership and shape overall product direction
• Build credibility as someone who can write or speak authoritatively on agentic AI
• Ship agent training and maturation system (skills & university) to production in first 12 months
• Define and document "what is an agent" framework adopted across the company
• Lead 3+ enterprise customer implementations and iterate product based on feedback
• Publish or present thought leadership on agentic AI externally
• Reduce ambiguity in agent architecture so engineers know what they're building

Key Qualifications

• Director-level Product Manager or Group Product Manager
• Hands-on experience building agentic AI systems (non-negotiable)
• ML/AI product background required with substantial depth in the field
• Experience with LLM training, prompt optimization, or agent frameworks (n8n, CrewAI, Sierra, etc.)
• Enterprise product sense: you understand org processes, compliance, separation of concerns
• Ability to go deep technically and explain agent architecture to engineers
• Coming from product organizations, not engineering or research

Preferred Experience

• Built vertical AI products serving enterprises
• Worked on agentic systems that went to production, not just prototypes
• Experience defining "what is an agent" at both the conceptual and implementation levels
• Background in skills training systems or model maturation platforms

Personal Characteristics

• Thought leader mindset with ability to influence industry direction
• Hands-on approach, not just strategic oversight
• Deep technical curiosity about agent architecture and capabilities
• Strong communication skills for explaining complex concepts
• Comfortable with ambiguity and defining new categories
• Bias toward shipping and learning from production deployments