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Member of Technical Staff - Engineering [Senior Product Manager] Bellevue | Hybrid NTT DATA AIVista, Inc., a wholly owned subsidiary of NTT DATA, is based in Silicon Valley. We develop AI products that operationalize AI across enterprises operating in complex regulatory environments. We partner with other NTT companies (NTT DATA Inc, NTT DATA Japan, NTT Docomo) to ensure successful deployment to NTT clients. Our clients benefit from AIVista’s deep product AI expertise combined with the industry domain and systems integration experience of NTT DATA. Role Description AIVista is a newly formed, product-focused company that combines deep expertise in AI science and engineering with the domain knowledge and enterprise reach of NTT DATA. The goal is straightforward: use agentic AI to solve hard, structural business problems in regulated industries, primarily insurance and financial services — and in doing so, genuinely transform how those industries operate. These sectors carry real complexity: dense regulatory requirements, legacy systems, high-stakes decisions, and meaningful accountability when things go wrong. That is precisely the kind of problem the platform is built to address. This role has meaningful scope. You will own two foundational platform capabilities end-to-end: how AI agents are orchestrated and governed at runtime, and how enterprise knowledge is structured to make those agents accurate and context-aware. Ownership here means the full arc, defining requirements, driving engineering execution, working through customer feedback, and iterating toward something that works in production. There is no handoff point where this becomes someone else’s problem. The team you will join has been building AI products for over a decade. Members come from both the Silicon Valley startup world and from hyperscalers, having built production AI systems well before large language models made the field mainstream. We have scientists who have done research that has shaped how generative AI is applied in production, engineers who have built some of the most widely used ML platforms, and product managers with experience shipping enterprise applications at scale. The problems we are working on are not solved. How do you enforce governance over AI agents operating autonomously in regulated workflows? How do you model enterprise knowledge in a way that survives messy, inconsistent source data? How do you deploy all of this in environments with strict data residency and air-gap requirements? These questions sit at the edge of what the industry knows how to do, and answering them well is what the platform is built around. Core Responsibilities · Own the product vision, roadmap, and success metrics for the AI agent orchestration and governance layer and the enterprise semantic data layer, maintaining clarity on scope, dependencies, priorities, and trade-offs across all active workstreams. · Translate ambiguous enterprise customer problems into crisp, actionable product requirements; PRDs, user stories, and acceptance criteria, that engineering teams can execute with confidence and minimal re-work. · Drive the full product lifecycle from discovery through GA: customer research, problem framing, specification, build oversight, QA alignment, launch readiness, and post-release iteration. · Serve as the primary product interface to engineering leads for both platform components, resolving scope ambiguities, making build-vs-buy-vs-configure decisions, and unblocking delivery without sacrificing quality. · Deeply engage with AI/ML architecture, API contracts, data pipeline design, and agent runtime behavior to identify risks and opportunities invisible to non-technical PMs. · Lead structured customer discovery with enterprise stakeholders across insurance and financial services clients, surfacing requirements, validating priorities, and establishing product-market fit for each platform capability. · Define and mainta