As a Senior Engineering Manager on the Walmart International Search ML Infrastructure team, you will be responsible for leading and managing a team of data platform engineers and ML engineers in building foundational platforms that power ML and search capabilities across all international markets. You will partner closely with data science teams, search engineering, Product, and international stakeholders to ensure we build scalable, multi-tenant, and high-performance infrastructure that accelerates time-to-market for ML models and enables data-driven decision making at global scale. About Team: Focusing on customer, associate and business needs, this team works with Walmart International, which includes more than 5,200 retail units, operating in 23 countries such as Canada, Central America, Chile, China, India, Mexico and South Africa to name a few. The Search ML Infrastructure team owns the foundational data pipelines, ML serving infrastructure, evaluation platforms, and experimentation frameworks that enable search and ML capabilities across all international markets. What you'll do: Provide guidance and lead a team of data platform engineers and ML engineers to build quality ML infrastructure solutions that process omni-channel engagement data, serve ML models at scale, and enable sophisticated evaluation and experimentation frameworks Ensure Software Quality Engineering by keeping the engineers up to date on best practices for ML operations, automated testing for data pipelines and ML systems, comprehensive monitoring and alerting, and taking a proactive approach to production ML infrastructure support Develops and implements ML platform development strategies using Agile development processes by using iterative and incremental development processes; collaborating between self-organizing, cross-functional teams for requirements and solutions; and promoting adaptive planning, evolutionary development and delivery for rapid and flexible response to changing ML and data science needs Drives the execution of multiple ML infrastructure initiatives and projects by identifying data science team pain points and operational needs; developing and communicating technical roadmaps and priorities; removing barriers and obstacles that impact ML model deployment speed; providing resources and technical guidance; identifying performance standards for data pipelines and ML systems; measuring progress and adjusting performance accordingly; developing contingency plans for production ML systems; and demonstrating adaptability and supporting continuous learning Work closely with data science teams, search engineering leads, international market stakeholders, and Product teams to ensure ML infrastructure work is aligned with business objectives and accelerates ML model time-to-market Own end-to-end ML infrastructure including parsers/stitchers for omni-channel data, multi-tenant data pipelines, feature engineering platforms, model serving infrastructure, evaluation systems (including site crawling, query sampling, NDCG evaluation), and experimentation frameworks