Sr Software Engineer - AI

Lowe's Companies, Inc.•Charlotte, NC

1d•Onsite

About The Position

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. 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.

Requirements

Bachelor’s degree in computer science, computer information systems (CIS), or related field or equivalent years of experience in lieu of education requirement, if applicable
5 years of experience in software development or a related field
4 years of experience in any of following competencies: frontend technologies (user interface/user experience), middleware (microservices and application programming interfaces), database technologies, or DevOps
4 years of experience working on project(s) involving the implementation of solutions applying development life cycles (SDLC) through iterative agile development
Apache Airflow
Cloud Composer
GCP cloud experience
Big Query
Trino/Presto
Experience working on Data Quality/Integrity theme
Tools like Great Expectations
Domain experience on retail forecasting or any other business forecast predictions/time series forecasting

Nice To Haves

Master’s degree in computer science, CIS, or related field
5+ years of experience developing software, data platforms, or AI/ML applications, including significant experience building and deploying production ML systems.
Experience leading technical design, implementation, and delivery of ML engineering projects
Experience designing, deploying, and operating production-scale ML systems, including live/batch inference, model versioning, monitoring, and lifecycle management.
Strong understanding of ML platform engineering, automation, and optimizing ML infrastructure for scalability, reliability, latency, and cost efficiency.
Ability to evaluate infrastructure tradeoffs and recommend effective solutions for ML deployment and operational workflows
MLOps tools such as MLflow, Kubeflow, or Vertex AI.
Foundational understanding of ML models.
Strong understanding of cloud platforms (GCP, AWS, or Azure).
Experience managing model registry and implementing versioning best practices.
Model serving frameworks
SQL
Python
PySpark
Big Data technologies (Hadoop ecosystem or cloud-native equivalents)
Analytics databases (Druid)
visualization tools (Superset)
CI/CD and Git

Responsibilities

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.
Perform root cause analysis, troubleshoot production issues, and participate in code reviews to improve code quality, reliability, and operational excellence.
Ensure AI/ML solutions comply with security, governance, privacy, and organizational standards throughout the development lifecycle.
Champion engineering best practices to continuously improve the reliability, scalability, maintainability, and operational efficiency of AI/ML platforms and pipelines.
Design and implement advanced ML infrastructure patterns, including feature stores, model registries, and automated model lifecycle workflows to enable faster and more reliable AI/ML delivery.
Partner with engineering and architecture teams to evaluate emerging AI/ML technologies, optimize system architectures, and drive continuous improvements in scalability, performance, and operational maturity.