(USA) Senior Director, Product Management

About this role

Position Summary...
What you'll do...
AI is reshaping how Walmart builds technology, serves customers and associates, operates the business, and makes decisions. As AI rapidly evolves across traditional machine learning, generative AI, multimodal models, and agentic systems, we are building an Enterprise AI Platform that provides the common foundation for AI workloads across Walmart.
The platform brings together capabilities spanning MLOps, model and inference access, LLM gateway services, developer experience, model lifecycle management, observability, governance, reliability, and AI economics. Its purpose is to help teams across Walmart build and operate AI solutions quickly, safely, reliably, and economically at enterprise scale.
As Senior Director of Product Management for the Enterprise AI Platform, you will define the product vision, strategy, and execution for one of the foundational platforms powering Walmart’s AI transformation.
This role requires more than traditional product management. You will need to understand where AI technology is heading, determine which capabilities should become durable enterprise platform primitives, and translate rapidly changing technology into a coherent product strategy. You will work closely with senior engineering leaders, architects, AI teams, infrastructure teams, governance and security partners, and business leaders to make decisions spanning models, infrastructure, developer experience, safety, economics, and platform architecture.
The ideal leader combines strong product judgment, meaningful technical depth, deep understanding of the AI ecosystem, strategic thinking, storytelling, executive influence, financial discipline, and a bias for action. You will bring clarity to ambiguity, stay grounded in customer and engineering realities, raise the bar continuously, and operate with the ownership of an entrepreneur and the maturity of an enterprise leader.
What You’ll Do

• Define the product vision and strategy: Establish the long-term direction for Walmart’s Enterprise AI Platform and translate Walmart’s AI ambitions into clear product priorities, platform capabilities, and investment decisions.

• Build for the evolution of AI: Anticipate shifts across foundation models, multimodal AI, agents, inference technologies, orchestration, and AI infrastructure, and ensure the platform evolves ahead of enterprise demand.

• Create durable platform abstractions: Determine where common capabilities should exist across models, providers, infrastructure, lifecycle management, governance, and developer workflows so teams can move faster while preserving flexibility and optionality.

• Drive model and infrastructure optionality: Enable teams to use the right models and infrastructure based on capability, quality, performance, cost, availability, and risk while minimizing unnecessary provider or technology lock-in.

• Create a world-class developer experience: Simplify how engineers, data scientists, and product teams discover AI capabilities, onboard, experiment, evaluate, deploy, observe, and operate AI workloads from development through production.

• Make cost a first-class product consideration: Build platform capabilities and product mechanisms that help teams understand and optimize model consumption, infrastructure utilization, unit economics, capacity, and cost-performance tradeoffs.

• Embed safety and governance into the platform: Partner with AI Governance, Security, Privacy, Legal, Compliance, and Risk teams to make enterprise controls, observability, and guardrails scalable and easy for engineering teams to adopt.

• Make the safe path the easiest path: Create platform defaults and paved roads that improve speed, reliability, security, governance, and cost efficiency at the same time.

• Own the AI ecosystem strategy: Continuously evaluate commercial models, open models, infrastructure providers, emerging AI technologies, and open-source ecosystems. Determine where Walmart should build, buy, partner, integrate, or standardize.

• Separate durable trends from hype: Develop informed perspectives on emerging AI technologies and identify which changes materially affect Walmart’s platform strategy and which do not.

• Understand customers deeply: Build strong relationships with engineering, product, data science, and business teams. Use customer feedback, product telemetry, adoption data, and direct engagement to identify friction and shape the roadmap.

• Drive adoption through product value: Build a platform teams choose because it makes them faster and more effective. Identify barriers to adoption and systematically remove them.

• Partner deeply with Engineering: Operate as a strategic counterpart to engineering leadership and engage credibly on architecture, APIs, inference, MLOps, orchestration, reliability, observability, capacity, and developer workflows.

• Turn ambiguity into direction: Frame complex problems, establish principles, evaluate tradeoffs, make decisions with incomplete information, and move teams from ambiguity to execution.

• Tell a compelling platform story: Translate complex technical concepts into clear narratives for engineers, product teams, senior executives, and business leaders, connecting platform investments to enterprise strategy and measurable outcomes.

• Prioritize ruthlessly: Balance foundational investments, customer needs, technology evolution, reliability, technical debt, safety, cost, and strategic bets while keeping the organization focused on the areas of highest enterprise leverage.

• Measure product outcomes: Establish metrics spanning adoption, onboarding, developer productivity, reliability, performance, utilization, cost efficiency, governance coverage, customer satisfaction, and business impact.

• Lead across organizational boundaries: Align Product, Engineering, Infrastructure, Governance, Security, Data, Finance, Procurement, and business teams around common priorities and decisions.

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