Director-Digital Product Management - Enterprise AI Platforms

American Express•Sunnyvale, CA

About The Position

The Director of Product Management will own the strategy, multi-year roadmap, investment priorities, delivery, adoption, and measurable outcomes for a portfolio within American Express’s Enterprise AI Platform. The role focuses on identifying enterprise-wide barriers to AI development and turning customer and Forward-Deployed Engineering learnings into reusable platform capabilities. The goal is to help teams move AI use cases from experimentation to secure, scaled production more quickly, easily, and cost-effectively. The Director will partner with Engineering, Architecture, Data Science, Security, Risk, business teams, and Forward-Deployed Engineering. They will lead product managers responsible for enterprise-scale AI capabilities, make build, buy, partner, and standardization decisions, and balance enterprise standards with flexibility for individual teams. Success includes increasing adoption and reuse of platform capabilities, improving developer productivity and platform economics, reducing the time and complexity of building and operating AI applications, and demonstrating business value.

Requirements

10+ years of progressive product management experience, including leadership experience building enterprise software, platforms, developer products, AI/ML products, or cloud technologies.
Proven experience defining product strategy and leading multi-year roadmaps for complex enterprise platforms.
Demonstrated success building and scaling products that enable other teams to develop differentiated customer or employee experiences.
Strong understanding of modern AI application architecture and the platform capabilities required to build, evaluate, deploy, govern, observe, and operate AI systems at enterprise scale.
Experience partnering deeply with Engineering to deliver highly technical platform capabilities.
Proven ability to influence senior stakeholders and drive alignment across complex, matrixed organizations.
Strong product judgment, including the ability to determine what should be standardized, abstracted, reused, purchased, or left to individual product teams while balancing customer, technical, operational, risk, and business considerations.
Excellent executive communication, storytelling, and presentation skills.

Nice To Haves

Experience leading product strategy for enterprise AI platforms, developer tools, ML infrastructure, or intelligent automation.
Experience building or scaling AI-powered products, LLM platforms, agentic systems, or AI orchestration capabilities.
Familiarity with modern AI platform capabilities such as model access and gateways, agent and tool integration, evaluation, retrieval and knowledge systems, prompt management, and AI observability.
Experience with enterprise AI governance, security, privacy, model risk management, and responsible AI.
Experience developing partnerships with hyperscalers, AI model providers, enterprise technology companies, or open-source communities.
Experience defining platform adoption strategies and measuring developer productivity, platform health, platform economics, and business value enabled.

Responsibilities

Own the product vision and strategy for a portfolio within the Enterprise AI Platform.
Identify recurring customer needs and translate enterprise AI and Forward-Deployed Engineering learnings into scalable platform opportunities.
Distinguish capabilities that should be standardized and reused from requirements that should remain application-specific.
Simplify the AI developer journey and establish scalable paved roads while preserving teams’ ability to innovate.
Evaluate emerging AI technologies and ecosystem shifts for their potential impact on customer outcomes, developer productivity, platform economics, enterprise reuse, and speed to production.
Own the product roadmap and prioritize investments based on customer value, developer productivity, technical feasibility, scalability, enterprise reuse, platform economics, and business value.
Lead product trade-offs and build-versus-buy-versus-partner decisions, including decisions about internal development, technology partners, and open-source ecosystems.
Determine where the platform should provide enterprise standards and abstractions versus where product teams should retain flexibility.
Align Product, Engineering, Architecture, Security, Risk, and business stakeholders around priorities, investments, and outcomes.
Lead the product lifecycle from discovery through delivery in partnership with Engineering, Architecture, and Forward-Deployed Engineering.
Deliver secure, scalable, and reliable platform capabilities, APIs, developer tooling, and reusable AI services that reduce the effort required to build and operate AI applications.
Balance innovation and speed with requirements for security, privacy, risk, responsible AI, reliability, and operational excellence.
Partner with Forward-Deployed Engineering to accelerate adoption, address high-value enterprise use cases, and identify platform gaps through engagement with internal customers.
Turn Forward-Deployed Engineering implementations and customer learnings into reusable platform capabilities and patterns, distinguishing repeatable enterprise needs from use-case-specific requirements.
Drive adoption and reuse across engineering teams and business units by improving developer experience and removing barriers to production.
Define product success metrics across adoption and reuse, developer productivity, time-to-production, platform health and reliability, platform economics, customer satisfaction, and business value.
Use customer feedback, platform telemetry, and Forward-Deployed Engineering insights to improve products and inform roadmap and investment decisions.
Measure whether investments reduce duplicated engineering effort, improve reuse, accelerate delivery, or improve the economics of developing and operating AI applications.
Build and lead a high-performing product organization, developing product managers and future product leaders.
Establish strong operating mechanisms, accountability, execution excellence, product practices, and decision frameworks.
Build a culture of customer focus, technical excellence, collaboration, and continuous learning.