Product Manager Technical - AI - Redmond, WA

About the role

The Product Manager Technical (AI) leads the strategy, roadmap, and delivery of AI-enabled products that drive business transformation, operational efficiency, and customer value. This role partners across engineering, data science, and business teams to identify high-impact opportunities, develop scalable AI solutions, and deliver measurable outcomes through responsible AI innovation.

Responsibilities

• Own the product vision, strategy, and multi-quarter roadmap for AI-enabled capabilities that improve customer, partner, and operational workflows.
• Work backward from customer and partner pain points to define AI product opportunities, business cases, product requirements, and measurable outcomes.
• Identify opportunities where AI/ML, Generative AI, agentic workflows, and intelligent automation can reduce friction, improve decision quality, increase operational speed, and scale business processes.
• Translate ambiguous business challenges into clear product requirements, acceptance criteria, launch readiness requirements, partner integration expectations, and success metrics.
• Partner closely with engineering, applied science, data science, analytics, UX, operations, legal, compliance, and external vendors to deliver AI-enabled products from concept through launch and continuous improvement.
• Influence upstream and downstream roadmaps where dependencies exist through strong judgment, technical depth, and effective stakeholder management.
• Define AI product evaluation frameworks, including quality benchmarks, regression criteria, human-in-the-loop review processes, model performance monitoring, feedback loops, and launch readiness gates.
• Establish instrumentation, dashboards, and inspection mechanisms to monitor user adoption, customer experience, partner engagement, model performance, operational health, and business impact.
• Drive experimentation strategies such as pilots, phased launches, workflow trials, A/B testing, and user feedback programs.
• Use data, customer insights, partner feedback, and technical constraints to make prioritization and trade-off decisions across competing initiatives.
• Incorporate responsible AI principles throughout the product lifecycle, including fairness, explainability, privacy and security, safety, controllability, transparency, veracity, and robustness.
• Build scalable operating mechanisms, including roadmap reviews, launch readiness reviews, risk and dependency tracking, executive narratives, decision documents, and post-launch business reviews.

Basic Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, Business, Mathematics, Economics, or a related field.
• 5+ years of experience in Product Management, Technical Product Management, Technical Program Management, or related roles owning technical products, platforms, or online services.
• Experience owning product strategy, roadmap definition, and feature prioritization for technical products or customer-facing systems.
• Experience working directly with engineering teams and participating in technical trade-off discussions involving architecture, APIs, data platforms, scalability, reliability, security, and integration design.
• Experience defining product requirements, success metrics, launch criteria, and post-launch measurement frameworks.
• Experience representing customer, business, and stakeholder needs during prioritization, planning, and delivery.
• Strong written and verbal communication skills with the ability to create product requirements documents, executive narratives, decision papers, and stakeholder communications.

Preferred Qualifications

• Experience developing, deploying, or managing AI/ML, Generative AI, agentic AI, or intelligent automation products at scale.
• Experience partnering with Applied Science teams, ML Engineering teams, Data Science teams, Analytics teams, and AI Platform teams.
• Experience translating model capabilities into customer-facing or operational product experiences.
• Experience with model evaluation approaches, including automated evaluation, human evaluation, quality benchmarking, regression testing, and responsible AI review mechanisms.
• Experience defining AI quality metrics such as correctness, safety, groundedness, robustness, latency, adoption, user satisfaction, cost-to-serve, and operational impact.
• Experience with cloud-based AI services, APIs, data platforms, enterprise integrations, and AI application development patterns.
• Experience with modern AI frameworks and services, including agent-based architectures, orchestration frameworks, memory systems, tool integrations, and LLM-powered applications.
• Experience incorporating responsible AI controls and governance practices, including safety, privacy, transparency, governance, robustness, and monitoring and steering AI behavior.