AI Product Manager
Redmond, WA(Hybrid)
Long term
Rate : $80/hr on CTC
• Identify where AI/ML, generative AI, agentic workflows, or intelligent automation can reduce friction, improve decision quality, increase speed, or scale operational processes.
• Translate ambiguous business problems into clear product requirements, acceptance criteria, launch readiness requirements, partner integration expectations, and measurable success metrics.
• Partner closely with engineering, applied science, data science, analytics, UX, operations, legal, compliance, and external vendor/partner teams to deliver AI-enabled products from concept through launch and continuous improvement.
• Define AI product evaluation frameworks, including quality benchmarks, regression criteria, human-in-the-loop review, model performance monitoring, feedback loops, and launchreadiness gates.
• Drive experimentation strategies such as pilots, phased launches, workflow trials, A/B testing, and feedback mechanisms to validate product assumptions and improve product performance over time.
• Establish instrumentation, dashboards, and inspection mechanisms to track adoption, customer experience, partner engagement, model performance, operational health, and business impact.
• Use data, customer insights, partner feedback, and technical constraints to make prioritization and trade-off decisions across competing roadmap opportunities.
• Incorporate responsible AI considerations into product design and lifecycle mechanisms, including fairness, explainability, privacy and security, safety, controllability, veracity and robustness, governance, and transparency.
• Build scalable operating mechanisms including roadmap reviews, launch readiness reviews, risk/dependency tracking, executive narratives, decision papers, and post-launch business reviews.
• Influence upstream and downstream roadmaps where dependencies exist, earning trust through clear writing, strong judgment, technical depth, and consistent delivery.
Basic Qualifications
• 5+ years of product management, technical product management, technical program management, or related experience 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 contributing to technical trade-off discussions related to architecture, APIs, data, scalability, reliability, security, or integration design.
• Experience defining product requirements, success metrics, launch criteria, and postlaunch measurement mechanisms.
• Experience representing customer, business, and stakeholder needs during prioritization, planning, and delivery decisions.
• Strong written and verbal communication skills, with the ability to create clear narratives, requirements, decision documents, and executive-ready updates. Preferred Qualifications
• Experience developing, deploying, or managing AI/ML, generative AI, agentic AI, or intelligent automation products at scale. Amazon’s AWS Marketplace AIagent PMT posting lists experience developing, deploying, and managing AI products at scale as preferred.
• Experience partnering with applied science, ML engineering, data science, analytics, or AI platform teams to translate model capabilities into customerfacing or operational product experiences.
• Experience with model evaluation approaches, including automated evaluation, human evaluation, quality benchmarking, regression testing, or responsible AI review mechanisms. AWS describes model evaluation, human-in-the-loop, safeguards, governance, and evaluation tools as part of responsible AI services and tools.
• Experience defining AI product quality metrics such as correctness, safety, groundedness, robustness, latency, adoption, user satisfaction, cost-to-serve, or operational impact.
• Experience with cloud-based AI services, APIs, data platforms, enterprise integrations, or AI application development patterns. Internal AWS GenAI learning material references Amazon Bedrock Agents, Strands Agents, and Amazon Nova as tools for building agentic AI solutions with APIs, orchestration, memory, and tool/system/data connections.
• Experience incorporating responsible AI practices into product development, including safety, privacy, transparency, governance, robustness, and mechanisms for monitoring and steering AI behavior
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