Product Manager Technical - AI - Redmond, WA

Product Manager Technical (AI)

Role Overview

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.

Key Responsibilities

Product Strategy & Roadmap

• 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
• Scale business processes

Product Definition & Delivery

• Translate ambiguous business challenges into clear:
• Product requirements
• Acceptance criteria
• Launch readiness requirements
• Partner integration expectations
• 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.

AI Product Quality & Evaluation

• Define AI product evaluation frameworks, including:
• Quality benchmarks
• Regression criteria
• Human-in-the-loop review processes
• Model performance monitoring
• Feedback loops
• Launch readiness gates
• Establish instrumentation, dashboards, and inspection mechanisms to monitor:
• User adoption
• Customer experience
• Partner engagement
• Model performance
• Operational health
• Business impact

Experimentation & Optimization

• Drive experimentation strategies such as:
• Pilots
• Phased launches
• Workflow trials
• A/B testing
• User feedback programs
• Use data, customer insights, partner feedback, and technical constraints to make prioritization and trade-off decisions across competing initiatives.

Responsible AI & Governance

• Incorporate responsible AI principles throughout the product lifecycle, including:
• Fairness
• Explainability
• Privacy and security
• Safety
• Cont rollability
• Transparency
• Veracity and robustness
• Governance
• Build scalable operating mechanisms, including:
• Roadmap reviews
• Launch readiness reviews
• Risk and dependency tracking
• Executive narratives
• Decision documents
• 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
• 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
• Integration design
• Experience defining:
• Product requirements
• Success metrics
• Launch criteria
• 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
• 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
• 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
• Responsible AI review mechanisms
• Experience defining AI quality metrics such as:
• Correctness
• Safety
• Groundedness
• Robustness
• Latency
• Adoption
• User satisfaction
• Cost-to-serve
• Operational impact
• Experience with:
• Cloud-based AI services
• APIs
• Data platforms
• Enterprise integrations
• AI application development patterns
• Experience with modern AI frameworks and services, including:
• Agent-based architectures
• Orchestration frameworks
• Memory systems
• Tool integrations
• LLM-powered applications
• Experience incorporating responsible AI controls and governance practices, including:
• Safety
• Privacy
• Transparency
• Governance
• Robustness
• Monitoring and steering AI behavior

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