Senior Product Manager – GenAI Data & AI Science

Amazon

Factoria (WA)

On-site

USD 151,000 - 205,000

Full time

11 days ago

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Job summary

Amazon is seeking a Senior Product Manager for Tech within FBA AI Science & Analytics to lead GenAI initiatives across data and science products and seller-facing experiences. You will define strategy, drive roadmaps, and partner with scientists, engineers, and product teams to deliver AI-native features that improve operational efficiency and growth for FBA worldwide.

You will own end-to-end product lifecycle from concept through launch, measure adoption and impact, and iterate quickly based on

Qualifications

Bachelor's degree in a quantitative/technical field such as computer science, engineering, statistics.
Experience owning/driving roadmap strategy and definition.
Experience with feature delivery and tradeoffs of a product.
Experience contributing to engineering discussions around technology decisions and strategy related to a product.
7+ years of technical product or program management experience.
Experience in representing and advocating for a variety of critical customers and stakeholders during executive-level prioritization and planning.

Responsibilities

GenAI Product Strategy & Roadmap: Define and execute the product vision for LLM-powered science and data agents, spanning prompt engineering strategies, retrieval-augmented generation (RAG) architectures, semantic parsing, and agent orchestration frameworks that enable autonomous reasoning at scale.
Semantic Layer & Knowledge Graph Design: Own the product requirements for FBA's centralized semantic layer, defining ontologies, entity-relationship schemas, disambiguation logic, and machine-readable metadata standards that LLMs consume to produce accurate, governed outputs.
LLM Agent Development: Partner with applied scientists and engineers to design, evaluate, and ship production LLM agents across multiple FBA domains for internal and seller-facing applications.
Stakeholder & Science Partnership: Work closely with the science and tech teams on model selection, fine-tuning strategies, retrieval pipeline optimization, and agent loop engineering.
Metrics & Evaluation: Define success metrics grounded in agent accuracy, query correctness, latency, coverage, and real time-savings.
Rapidly Evolving AI Landscape: Stay current with advances in foundation models, agentic architectures, harness engineering, semantic parsing, and enterprise AI tooling.