亚马逊Sr. AI Product Manager, Seller Compliance

Product Strategy & Development * Own the product vision, strategy, and roadmap for AI-powered Seller Education experiences. * Identify and prioritize learning opportunities across the seller lifecycle (e.g., onboarding, growth, compliance, optimization). * Design and launch AI solutions (e.g., adaptive learning, content recommendation, conversational tutors) that improve learning outcomes and engagement. * Drive measurable improvements in Seller learning efficiency, satisfaction, and business impact. * Lead continuous product improvement using experiments, data insights, and qualitative feedback from Sellers and stakeholders. Technical Leadership * Partner with data science and engineering teams to design, build, and scale AI/ML and LLM-based learning features. * Define clear technical requirements, success metrics, and guardrails for AI-powered education workflows. * Oversee end‑to‑end product development cycles, from discovery and design through implementation and launch. * Promote data-informed decision making and experimentation across all product initiatives. Stakeholder & Program Management * Lead cross‑functional collaboration between product, program, tech, content, marketing, and operations teams. * Communicate product strategy, roadmap, and results to senior leadership, highlighting trade‑offs and impact. * Drive alignment and change management across CN and global teams to scale adoption of AI‑powered learning solutions. * Influence internal and external partners to co‑create high‑quality, localized, and relevant learning experiences for Sellers.

Technical Development & Architecture * Design and implement scalable AI/ML solutions for Compliance use cases * Lead the development of efficient ML models and end‑to‑end data processing pipelines from ingestion to serving. * Build robust, production-grade AI services using Python and modern ML frameworks. * Make and document sound architectural decisions, ensuring systems are scalable, secure, and cost‑effective. * Establish and maintain high engineering standards, including testing, monitoring, and documentation. Engineering Leadership * Partner closely with data scientists, product managers, and operations teams to deliver end‑to‑end AI/ML solutions. * Define and evolve the technical architecture for AI-powered features and platforms. * Lead code reviews, enforce best practices, and elevate engineering quality across the team. * Continuously improve AI system performance, reliability, and latency through experimentation and optimization. Technical Collaboration & Operations * Work with cross‑functional partners to understand requirements, refine scope, and prioritize technical work. * Provide technical guidance and mentorship to junior and mid‑level engineers. * Collaborate with platform and DevOps teams to ensure smooth deployment, monitoring, and maintenance of AI systems. * Implement and evolve ML Ops practices (e.g., CI/CD for models, feature stores, model monitoring, and retraining workflows).

Technical Development & Architecture * Design and implement scalable AI/ML solutions for Compliance use cases * Lead the development of efficient ML models and end‑to‑end data processing pipelines from ingestion to serving. * Build robust, production-grade AI services using Python and modern ML frameworks. * Make and document sound architectural decisions, ensuring systems are scalable, secure, and cost‑effective. * Establish and maintain high engineering standards, including testing, monitoring, and documentation. Engineering Leadership * Partner closely with data scientists, product managers, and operations teams to deliver end‑to‑end AI/ML solutions. * Define and evolve the technical architecture for AI-powered features and platforms. * Lead code reviews, enforce best practices, and elevate engineering quality across the team. * Continuously improve AI system performance, reliability, and latency through experimentation and optimization. Technical Collaboration & Operations * Work with cross‑functional partners to understand requirements, refine scope, and prioritize technical work. * Provide technical guidance and mentorship to junior and mid‑level engineers. * Collaborate with platform and DevOps teams to ensure smooth deployment, monitoring, and maintenance of AI systems. * Implement and evolve ML Ops practices (e.g., CI/CD for models, feature stores, model monitoring, and retraining workflows).

• Engage with customer IT and business leaders to understand their application, data, and AI priorities, and design secure, scalable solutions that drive business value and customer satisfaction. • Lead technical engagements across architecture design, Proof of Concepts (POCs), and Minimum Viable Products (MVPs) to accelerate adoption of Azure AI, App Services, GitHub, and data platforms. • Own the end-to-end technical delivery results, ensuring completeness and accuracy of consumption and customer success plans in collaboration with the CSAM. • Drive next best actions and generate incremental pipeline from each engagement, aligning with Unified Enterprise Support (ES) priorities. • Deliver repeatable intellectual property (IP) and contribute to centralized IP development to accelerate deployment and achieve targeted outcomes. • Provide delivery oversight and escalation support for key Factory engagements across AI and App Innovation projects. • Lead the health, resiliency, security, and optimization of mission-critical workloads, ensuring readiness for production-scale AI use cases. • Act as the Voice of the Customer by sharing insights and feedback with engineering teams to influence product improvements and remove adoption blockers. • Support customer skilling through technical workshops, readiness activities, and recommendations that ensure solution performance, maintainability, and reliability. • Maintain deep technical expertise and stay current with Azure, AI, GitHub, and cloud-native development trends, while contributing to internal and external technical communities. • Be accredited and certified to deliver with advanced and expert-level proficiency in priority workloads including Azure AI Foundry, AKS, App Service, Cosmos DB, Azure SQL, PostgreSQL, APIM, and GitHub. • Demonstrate a growth mindset by continuously aligning your skills to customer needs, contributing to knowledge sharing, and mentoring others to accelerate customer outcomes.