Staff, Software Engineer - ML Engineer

Requirements

• Expert-level proficiency in programming languages such as Python or Java, with a strong track record of delivering high-performance, production-grade code for machine learning applications.
• Proven experience designing, developing, and operationalizing end-to-end ML systems, including data ingestion, feature engineering, model training, deployment, and monitoring.
• A solid understanding of core machine learning principles, including various model families (e.g., tree-based models, deep neural networks), feature selection techniques, and practical challenges such as data quality, bias, and concept/data drift.
• Deep experience in Linux-based development environments, along with containerization using Docker and orchestration with Kubernetes for scalable ML service deployment.
• Hands-on experience working with cloud platforms like Google Cloud Platform (GCP) or Microsoft Azure, with familiarity in their AI/ML tools, infrastructure components, and DevOps ecosystems.
• Proficiency in building and scaling API-based services and distributed systems, and in managing workflows using tools such as Apache Airflow or similar orchestration frameworks.
• Strong working knowledge of SQL and modern data engineering practices, enabling seamless integration of ML pipelines with batch and streaming data sources.
• Proficient in version control with Git, and experienced in collaborating within agile, cross-functional software teams.
• A deep grasp of software engineering best practices, including API design, automated testing, CI/CD pipelines, and observability for ML-driven systems.
• Clear and confident communication skills, with the ability to understand cross-functional requirements and translate them into scalable technical solutions.
• 5+ years of industry experience, with a demonstrated ability to take ownership of complex projects and deliver impactful AI/ML solutions from concept to production.
• A mindset grounded in intellectual curiosity, with a passion for MLOps, cloud-native technologies, and driving innova

Additional Information

Position Summary... What you'll do... Walmart employs more than 2.3 million associates worldwide, with 1.6 million associates in the U.S. Each year, Walmart hires 500,000 applicants to fill thousands of job profiles from engineers, designers, and marketers to pilots and buyers. We promote over 300,000 people annually to positions of greater responsibility. About People Technology 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 focus on what matters most - supporting our customers and members. People Technology is a significant segment of Walmart Global Tech's Enterprise Business Services, invested in building a compact, robust organization that includes service operations and technology solutions for Finance, People, and the Associate Digital Experience. About People.AI Team The People.AI team is responsible for developing and deploying AI/ML solutions supporting the Walmart associates globally. In this role, you will build an LLM-powered intelligent experience within a chatbot or business application to enhance associate experience and productivity. You will design and build an intelligent conversational interface that improves communication, automates tasks, accesses data and insights, and provides personalized Q&A support to associates, ultimately creating a more efficient and engaging work environment. Job Description Design, develop, and deploy LLM-powered intelligent applications that enhance productivity and engagement for Walmart associates across the globe. Build and integrate enterprise-grade conversational agents and AI copilots into chatbots, internal portals, and business applications to automate tasks, improve communication, and provide real-time, contextualized support. Leverage state-of-the-art LLMs and frameworks like LangChain, LangGraph to create dynamic, natural language interfaces over enterprise data. Implement retrieval-augmented generation (RAG) pipelines using vector databases such as Milvus or FAISS to enable deep integration with Walmart's knowledge ecosystem. Enhance the clarity, conciseness, and readability of generated knowledge Q&A to ensure it is well-suited for voice-based service agents. Build developer-friendly tools and reusable frameworks that abstract key aspects of ML and LLM system complexity-enabling developers without prior ML experience to quickly build high-quality, scalable AI solutions with minimal friction, while maintaining reliability, governance, and performance standards. Act as a platform enabler and technical collaborator, working closely with Data Scientists, Software Engineers, Product Managers, and MLOps/DevOps teams to streamline model deployment, monitoring, and lifecycle management.

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