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
  • Controllability
  • 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