Senior Manager, Data Science

Position Summary...

What you'll do...

This Senior Manager-level role is a strategic technical leader within Walmart's eCommerce Analytics Platform & Engineering team. This Senior Manager, Data Scientist will drive the vision, architecture, and implementation of Walmart's next-generation AI infrastructure, GenAI platforms, and engineering capabilities that power intelligent automation, AI agents, and real-time decisioning at global scale.

You will lead the end-to-end design of high-performance AI systems-including model training and serving infrastructure, distributed inference pipelines, data and feature platforms, retrieval/grounding services, and automation frameworks that support safe, reliable, and scalable GenAI across the enterprise.

This role requires a rare combination of deep technical expertise, systems design excellence, and the ability to influence engineering, data science, and product teams at the highest levels.

Responsibilities

AI Infrastructure Architecture & Leadership
• Serve as Walmart's principal architect for enterprise AI infrastructure, defining patterns for distributed training, vector retrieval systems, LLM inference optimization, and scalable agent backends.

* Lead design of GenAI platform components, including: o Orchestration and lifecycle management for LLMs and multimodal models o Retrieval-augmented generation (RAG) pipelines o Vector search and embedding pipelines o Model serving, autoscaling, and high-availability inference systems

* Architect end-to-end platforms using Vertex AI, Google Cloud AI infrastructure, and Azure AI services where applicable. GenAI Platform Development & Engineering
• Build and evolve core GenAI platform capabilities used across eCommerce Analytics: o Agent orchestration and backend frameworks o Tool/action routing services o System prompts, grounding, and context management o Observability, evaluation, safety, and feedback loops
• Partner with product engineering teams to integrate GenAI solutions into production facing commerce, analytics, and automation experiences.
• Lead strategic evaluations of emerging AI technologies and drive their adoption across Walmart's AI ecosystem.

Distributed Systems, AI Performance & Scale

* Architect and optimize high-scale data and inference pipelines running on Dataproc, Kubernetes, Airflow, and cloud-native distributed systems.

* Improve performance of AI workloads through advanced techniques: o Parallelized preprocessing & feature generation o Distributed training optimization o Token-level and batch-level inference acceleration o Memory and compute efficiency strategies for LLMs

* Lead operational excellence across AI systems including monitoring, incident response, load forecasting, and automated self-healing. AI/ML Engineering Standards & Technical Influence

* Define engineering standards for AI observability, versioning, deployment, model telemetry, and responsible AI integrations.

* Mentor senior-level data scientists and engineers on best practices in AI infrastructure, scalable ML engineering, and GenAI systems design.

* Provide domain expertise to help shape Walmart's enterprise-wide strategies for AI adoption, AI governance, and future-facing platform technology. Skills & Experience AI Infrastructure & Distributed Systems Expertise

* Deep experience architecting distributed AI/ML systems, including training clusters, inference serving layers, and retrieval-augmented systems.

* Expert-level understanding of large-scale compute, networking, cluster orchestration, cloud infrastructure, and performance engineering.

* Hands-on experience building and optimizing: o LLM inference systems o Embedding & vector search pipelines o Model serving & batch/streaming inference o Real-time AI agent backends AI/ML Platform Tools & Cloud Technologies

* Vertex AI (Pipelines, Predictions, Feature Store, Vector Search, Search, Model Registry, Workbench)

* Google Cloud AI infrastructure, SDKs, Cloud Build, Artifact Registry, IAM, Cloud Run, Cloud Functions

* Big Query, Presto, Cloud SQL, Cassandra, MongoDB

* Graph DB technologies such as Neo4j for relationship modeling and knowledge graph based retrieval

* Microsoft Fabric &…