(USA) Principal, Software Engineer

Position Summary...

What you'll do...

Are you passionate about pioneering cutting-edge technology combining mobile, data, and AI to revolutionize Walmart's associate (employee) experiences? Do you dream of creating innovative systems and products that make a big impact to more than 2 million users across the globe?

As a Walmart Distinguished Data Scientist, you will play a pivotal role in designing, developing, and implementing digital solutions that solve mission critical business problems in supporting Walmart associates. 

About Team:

The Enterprise People Technology team supports the successful deployment and adoption of new People technology across the enterprise. As a Fortune #1 company, our work impacts millions of associates globally. We strive to continuously improve people technology and products to help managers and associates so they can focus on what matters most - supporting our customers and members. People Technology is one of the major segments of Walmart Global Tech’s Enterprise Business Services, which is invested in building a compact, robust organization that includes service operations and technology solutions for Finance, People, and the Associate Digital Experience.

What You Will Do

• Develop and deploy machine learning models using decision trees, random forests, and gradient boosting techniques (e.g., XGBoost), with a focus on scalability, interpretability, and hyperparameter optimization.

• Apply unsupervised learning techniques such as topic modeling (LDA, BERTopic) and clustering (K-means, HDBSCAN) to extract insights from unstructured data.

• Build and refine Retrieval-Augmented Generation (RAG), Conversational-Aware Generation (CAG), and Knowledge-Augmented Generation (KAG) systems, utilizing advanced chunking, hybrid retrieval, and reranking strategies.

• Design and orchestrate multi-agent LLM systems using paradigms like ReAct, self-reflection, and plan & execute, integrating tool usage and agent-to-agent communication protocols.

• Fine-tune and optimize large language models using methods like LoRA, QLoRA, quantization, prompt tuning, and distillation.

• Develop automated and human-in-the-loop evaluation frameworks for NLP tasks such as summarization, question answering, and code generation.

• Work across cloud-native AI platforms (Azure, AWS, GCP) to deploy scalable ML/AI solutions, using modern MLOps tools to streamline experimentation and production workflows.

What You Will Bring

• Deep expertise in traditional ML techniques and frameworks, with a strong foundation in model interpretability, tuning, and real-world deployment.

• Proficiency in unsupervised learning methods for organizing and deriving value from unstructured datasets.

• Demonstrated experience with generative AI and retrieval-augmented architectures, including knowledge of advanced search and reranking techniques.

• Solid understanding of agent-based LLM frameworks and protocols (LangChain, LangGraph, AutoGen, MCP, Agent Protocol), enabling you to build sophisticated multi-agent systems.

• Hands-on experience fine-tuning LLMs using cutting-edge methods for improved efficiency and performance.

• A strategic approach to evaluation, with a track record of building robust pipelines for assessing model performance both automatically and with human feedback.

• Technical fluency across Python, PyTorch, TensorFlow, Hugging Face, and popular vector databases (Milvus, Pinecone, Qdrant), combined with experience on major cloud AI platforms and MLOps tools like MLflow, Docker, and Kubernetes.

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