Senior Manager, Product Management, AI Platform

Intuitive; Sunnyvale (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Intuitive is seeking an experienced AI Product leader to own the strategy, roadmap, prioritization, and release planning for our enterprise Generative AI platform. The role sits at the intersection of business needs, platform strategy, and engineering execution, translating a growing set of requests into coherent platform capabilities and deliverables.

The ideal candidate combines enterprise product management excellence with deep knowledge of Generative AI, data, and agentic technologies to

Qualifications

• Ideal candidate combines enterprise product management with deep Generative AI, data, and agentic tech, enabling clear requirements and informed trade-offs.
• Experience translating portfolio requests into platform capabilities and engineering deliverables.

Responsibilities

• Own and evolve the Enterprise AI Platform product vision, strategy, and roadmap.
• Translate business priorities into platform capabilities, user stories, acceptance criteria, and engineering deliverables.
• Prioritize feature requests based on impact, reach, alignment, feasibility, risk, and dependencies.
• Define release plans, sequencing, milestones, and delivery commitments with AI Engineering leadership.
• Drive adoption, usability, and time-to-value across knowledge bases, Text-to-SQL, agents, and platform services.
• Partner with governance, security, privacy, legal, and infrastructure teams to meet enterprise standards.

Skills

Product strategy

Roadmap development

Prioritization

Release planning

Enterprise AI

Cross-functional leadership

AI platform strategy

Engineering collaboration

Tools

Text-to-SQL

Job description

Job Description

We are seeking an experienced AI Product leader to own the product strategy, roadmap, prioritization, and release planning for our enterprise Generative AI platform. The platform provides a secure, scalable environment for building Generative AI solutions using company-confidential data and enterprise systems.

This role will operate at the intersection of business needs, AI platform strategy, and engineering execution. The successful candidate will translate a growing portfolio of feature requests and business requirements into clear platform capabilities and engineering deliverables, establish priorities based on enterprise value, and partner closely with AI Engineering, Data Science, and business-facing teams to deliver a coherent and high-impact AI platform roadmap.

This is an ideal role for a product leader who combines strong enterprise product management skills with sufficient technical depth in Generative AI, data, and agentic technologies to effectively shape requirements and make informed product trade-offs.

The Enterprise AI Platform

The platform supports a growing set of enterprise AI capabilities, including:

• Secure knowledge and unstructured data: Upload, process, chunk, index, and interact with company-confidential unstructured content using approved foundation models.




• Enterprise Knowledge Bases: Create and access a growing repository of governed knowledge bases for business functions and enterprise users.




• Natural language access to structured data: Enable users and AI agents to ask business questions in natural language and access governed Enterprise Data Warehouse data through Text-to-SQL capabilities.




• AI agents and workflow automation: Build and deploy agents that automate or augment enterprise business workflows.




• Shared AI services: Provide reusable capabilities such as translation and other common AI services.




• Model choice and platform evolution: Support approved foundation models and continuously incorporate new AI capabilities as technologies and business needs evolve.




• 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.