Senior Manager, Product Management, AI Platform

Key Responsibilities 1. Product Strategy & Roadmap Own and evolve the product vision, strategy, and roadmap for the Enterprise AI Platform. Translate Enterprise AI strategy and business priorities into a coherent set of platform capabilities and product investments. Continuously assess emerging AI technologies and product patterns and determine where they should influence the platform roadmap. Balance near-term business needs with platform scalability, reuse, security, maintainability, and long-term architectural direction. 2. Requirements Translation & Product Definition Partner with business-facing Data & Analytics teams, business stakeholders, and technical teams to understand new use cases and capability requests. Translate business-level requirements into well-defined platform capabilities, user stories, acceptance criteria, workflows, and engineering deliverables. Clarify the problem to be solved, target users, expected business value, data requirements, dependencies, and measures of success before committing engineering capacity. 3. Prioritization, Backlog & Release Management Own the product backlog and establish a transparent framework for evaluating and prioritizing feature requests. Prioritize investments based on business impact, user reach, strategic alignment, technical feasibility, risk, dependencies, and engineering effort. Partner with AI Engineering leadership to define release plans, sequencing, milestones, and delivery commitments. Manage competing stakeholder priorities and communicate product decisions, trade-offs, roadmap changes, and release expectations clearly. 4. Platform Adoption & Product Experience Develop a deep understanding of how employees and business teams use the platform and identify opportunities to improve usability, discoverability, adoption, and time-to-value. Define product success metrics and use platform telemetry, user feedback, adoption data, and business outcomes to guide roadmap decisions. Drive consistent product experiences across knowledge bases, Text-to-SQL, agents, model access, translation, and other platform services. Partner with enablement and support teams to improve onboarding, documentation, release communications, and user education. 5. Cross-Functional Leadership & Governance Serve as the primary product partner to AI Engineering and Data Science teams, ensuring engineering execution remains aligned with product priorities and user needs. Partner with AI & Data Governance, Security, Privacy, Legal, and Infrastructure teams to ensure platform capabilities meet enterprise standards. Ensure new features incorporate appropriate security, access controls, responsible AI, data governance, observability, and operational requirements from the outset. Build strong relationships across business functions and create mechanisms for structured intake, feedback, prioritization, and roadmap communication.   Distinguish between reusable platform capabilities and one-off use-case requirements, driving reuse and standardization wherever appropriate. Qualifications 8+ years of product management experience, including leadership of enterprise software, data, analytics, AI/ML, cloud, or platform products; level will be calibrated based on experience and scope. Demonstrated success owning complex product roadmaps and translating ambiguous business needs into clear, executable engineering requirements. Experience prioritizing large backlogs across multiple stakeholders and making disciplined trade-offs among business value, user needs, technical complexity, and platform strategy. Strong understanding of Generative AI concepts and enterprise AI patterns, including LLMs/foundation models, retrieval-augmented generation and knowledge bases, structured-data access/Text-to-SQL, and AI agents. Experience with AI/ML infrastructure, data platforms, or cloud services (e.g., model training, model serving, feature stores, vector search, LLM infrastructure, ML pipelines). Working knowledge of cloud platforms, APIs, enterprise data environments, identity/access controls, and modern software delivery practices. Ability to engage credibly with AI engineers, data scientists, architects, and security teams while communicating effectively with non-technical business stakeholders. Strong written and verbal communication, stakeholder management, and executive presentation skills. Proven ability to operate effectively in a rapidly evolving environment where technologies, user expectations, and priorities change quickly. Deep technical background — CS, EE, or equivalent degree strongly preferred; former software engineer experience is a significant plus. You should be comfortable going deep on system architecture, writing technical specs, and engaging credibly with world-class AI engineers. Preferred Experience Experience managing an internal enterprise AI platform, developer platform, data platform, or other shared enterprise technology product. Experience with enterprise GenAI implementations involving confidential or sensitive company data. Experience with agentic AI, workflow automation, semantic layers, enterprise search/RAG, or natural-language access to structured data. Experience establishing product operating mechanisms such as intake processes, prioritization frameworks, product councils, roadmap reviews, release planning, and adoption metrics. What Success Looks Like A clear, business-aligned product strategy and roadmap for the Enterprise AI Platform. A disciplined and transparent process for intake, prioritization, backlog management, and release planning. Business requests are translated into high-quality product requirements that enable AI Engineering and Data Science teams to execute efficiently. Engineering capacity is increasingly directed toward reusable platform capabilities that create value across multiple business functions. Platform adoption, user experience, reliability, and measurable business value improve over time. Business stakeholders