(USA) Distinguished, Data Scientist - Agentic AI Systems Engineering & Model Post-Training

Position Summary... What you'll do... The Opportunity: Walmart’s Supply Chain AI Lab & Innovation Factory is building a new generation of production-grade agentic AI systems that reason over complex enterprise information, coordinate specialized agents, use tools safely, plan and execute long-horizon work, and continuously improve through rigorous evaluation and model post-training. This role exists to build those systems end to end—and to improve the models that power them. This is not an analytics-focused data science role. It is a deeply hands-on AI systems engineering position focused on designing, building, and operating production software. As the Distinguished technical leader in this space, you own production systems end to end across the full technology stack—agent orchestration and model logic, backend services and APIs, the data layer, and web and CLI/TUI interfaces. Through exceptional judgment, practical invention, mentoring, and code, you set technical direction and shape the architecture of the shared agentic platform and its model-improvement system. What you will build and own: Build advanced agentic systems end to end • Own the architecture, implementation, evaluation, launch, and operation of complex, autonomous multi-agent applications that tackle our highest-value enterprise initiatives—from the first working full-stack proof of concept through dependable, production-scale service. • Build systems in which specialized agents can reason, plan across long horizons, coordinate work, use tools, retrieve enterprise knowledge, maintain secure and durable session and workflow state, manage context growth, ask for human approval when needed, and cancel, resume, replay, or recover safely from failure. • Build a policy-first agent runtime and control plane with deterministic allow, deny, and ask decisions; least-privilege tool and data access; scoped identity and authorization; auditable human approvals; bounded subagent delegation; and safe cancellation, user steering, and retry behavior for long-running work. • Own the full product path as a full-stack systems engineer: agent and model logic, backend services and APIs, the data layer (database and schema design, data modeling, migrations, and data integrations), telemetry, monitoring, and thoughtful React/TypeScript experiences for associates and operators. Where the workflow demands it, design equally usable AI-native CLI/TUI or headless, structured-output interfaces that support automation, operations, and CI/CD-style integration. Where you will apply this These capabilities will first be applied to some of Walmart's most complex operational and supply-chain problems, beginning with the Autonomous Supply Chain Engine and its Discovery Loop—a continuous, human-in-the-loop mechanism that monitors signals, discovers opportunities and risks, reasons through changing conditions, and generates strategic recommendations. • Build and own the Discovery Loop within our Autonomous Supply Chain Engine. This is the flagship capability this role exists to build: you will own its execution model, its knowledge and context layer, its evaluation loop, and the learning path that turns human feedback and business outcomes into measurable improvement. • Partner directly with supply chain experts, scientists, data owners, operations, and engineering stakeholders to discover the real workflow, prototype rapidly with real users, measure value and risk, and harden successful solutions without losing speed. This is a high-visibility builder role. You will work closely with the SVP of Supply Chain Strategy, the head of AI for Supply Chain Strategy, and senior leaders across supply chain strategy, operations, data, and technology—bringing grounded technical insight to strategic priorities and turning them into responsibly operated products. Visibility here is earned through working software, sound judgment, clear communication, and measurable outcomes. Prior supply chain or logistics experience is not required. We will help you learn the operational domain; you bring outstanding depth in agentic AI engineering, software engineering, and machine learning engineering, plus the curiosity and ownership to turn unfamiliar problems into durable products. Engineer the agentic AI foundation • Own the design and build of advanced multi-agent harnesses, runtimes, and orchestration capabilities using frameworks such as Pydantic AI, LangGraph, LangChain, AutoGen, or LlamaIndex, or purpose-built custom infrastructure. Building your own runtime where that is the right call is a strength, not a gap. Establish typed contracts, testability, scalability, operability, and developer ergonomics as non-negotiable platform properties. • Engineer explicit, inspectable agent execution. Design graph-based, state-machine-based, event-driven, planner/executor, or equivalent execution models as the problem warrants, with explicit state, typed dependencies and handoffs, conditional branching, fan-out and fan-in to subagents, retry and repair paths, loop detection, cost and time budgets, and hard termination conditions. Design external verification into the system—test runners, execution results, transaction outcomes, deterministic checks, and expert human review—because a model reviewing its own output is not verification. • Build governed knowledge and context infrastructure for durable agent memory. Choose and combine the right representations—knowledge and context graphs, vector retrieval, relational and temporal models, or hybrids—with an explicit schema and ontology treated as a product contract. Own entity resolution and conflict handling, construction and enrichment pipelines from structured and unstructured sources, provenance and temporal validity, schema validation and evolution, and multi-hop retrieval that answers questions flat retrieval cannot. • Build and operate agent skills, tool adapters, hooks and extension points, Model Context Protocol (MCP) clients