Principal Product Manager, Core AI Platform

About the role

You will define the foundational AI infrastructure that enables intelligent experiences across the entire Qualtrics product suite. This role involves managing the lifecycle of critical platform capabilities, including agent orchestration, semantic systems, and AI safety, to ensure internal teams can build scalable and reliable products.

Key facts

What you'll do

• Develop and execute the product strategy for the Core AI Platform.
• Define architecture for agent infrastructure, including planning, tool use, and multi-agent patterns.
• Build systems for AI observability, debugging, and model abstraction to manage latency, cost, and reliability.
• Create frameworks for AI evaluation, such as task success measurement, regression testing, and human-in-the-loop systems.
• Manage ontologies, semantic layers, and context engineering to ground AI in enterprise data.
• Establish governance, permissions, and guardrails to meet enterprise security standards.
• Partner with engineering, data science, and research teams to prioritize platform investments.
• Communicate roadmaps and vision to senior leadership and internal stakeholders.
• Define KPIs for developer velocity, platform adoption, and AI quality.

Requirements

• Bachelor's degree in Computer Science, Engineering, Data Science, Business, or a related field.
• 8+ years of product management experience, specifically with complex platforms, data systems, or AI/ML infrastructure.
• Proven history of defining strategy and delivering technical products in collaboration with research or engineering teams.
• Understanding of enterprise requirements including privacy, security, scale, and explainability.
• Ability to bridge the gap between high-level vision and detailed technical architecture.
• Strong communication skills to influence cross-functional teams and senior leaders.

Nice to have

• Experience with agentic AI systems and orchestration.
• Background in knowledge graphs, metadata systems, or semantic layers.
• Hands-on work with LLM infrastructure, model gateways, or inference platforms.
• Experience building developer platforms for internal or external users.
• Familiarity with AI safety, reliability, and experimentation frameworks.

Skills & tools

• Agentic AI architecture
• Context engineering and memory systems
• AI evaluation and observability frameworks
• Enterprise AI governance and security
• Product roadmap and investment planning

Practical notes

• Annual $1,800 experience bonus for personal use.
• 10% of work time dedicated to personal learning and development.