Sr Software Engineer - AI - Lowe's Home Improvement

Innovate in Charlotte

Thank you for dedicating your time and talent to Lowe’s. We want to give you more opportunities to learn and grow, so if you find a position you’re interested in below, we encourage you to apply!

Your Impact The primary goal is to generate AI/ML forecasts that help the business plan for future demand, optimize resources, reduce risk, and make data-driven decisions. Lowe's forecasting platform team is responsible for predicting future trends, outcomes, or events based on current and historical data.

Work with a Winning Team As part of a Fortune 50 company and retail leader, your work can change an entire industry. Our CEO is a forward-thinker when it comes to tech, and with one of Forbes Top 50 CIOs leading the charge, you can come to work knowing you’ll have access to the data, tools, and support that few other companies can offer. We also know what it takes to create an inclusive culture that supports you. Our teams are structured around the engineer, giving you the support you need to do your best work. Since we’ve been in business for over 100 years, we’ve built an excellent track record of growth and success. There’s peace of mind knowing you have the stability and resources you need to focus on solving tough challenges. And as you solve these challenges, know you’ll be surrounded by supportive associates with curious minds who listen to you, respect you, and recognize your hard work.

What You Will Do

Build and maintain scalable data ingestion, transformation, feature engineering, and post-processing pipelines to support model training, inference, analytics, and reporting.

Collaborate with Data Scientists, Product, and Business teams to translate business requirements into scalable AI/ML solutions and production-ready data pipelines.

Design, develop, deploy, and maintain end-to-end ML systems, including model packaging, registration, versioning, deployment, and serving using platforms such as MLflow, Vertex AI, Kubeflow, or similar MLOps tools.

Build and optimize batch and real-time inference pipelines for performance, scalability, reliability, and cost efficiency across cloud and on-premises environments.

Implement and maintain CI/CD pipelines and workflow orchestration for AI/ML models and data pipelines using DevOps and MLOps best practices.

Design and implement monitoring frameworks to track model performance, data quality, data drift, model drift, and production system health.

Build reusable libraries, utilities, and platform components that improve engineering productivity and standardize ML development across teams.

Develop production-quality software components following software engineering best practices, ensuring solutions are scalable, testable, maintainable, and efficient.