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

+…