Software Engineer_Java_ReactJS_Data&ML

Innovate in Bengaluru This position is based at our on-site office in Bengaluru. Lowe’s offers an ultramodern work environment, complete with cutting-edge technology, collaborative workspaces, an on-site gym and clinic, and other perks to enhance your work experience.

About Lowe’s Lowe’s Companies, Inc. (NYSE: LOW) is a FORTUNE® 100 home improvement company serving approximately 16 million customer transactions a week, with total fiscal year 2024 sales of more than $83 billion. Lowe’s employs approximately 300,000 associates and operates over 1,700 home improvement stores, 530 branches and 130 distribution centers. Based in Mooresville, N.C., Lowe’s supports the communities it serves through programs focused on creating safe, affordable housing, improving community spaces, helping to develop the next generation of skilled trade experts and providing disaster relief to communities in need. For more information, visit Lowes.com.

At Lowe’s India, we are the enablers who help create an engaging customer experience for our $90+ billion home improvement business at Lowe’s. Our 4000+ associates work across technology, analytics, business operations, finance & accounting, product management, and shared services. We leverage new technologies and find innovative methods to ensure that Lowe’s has a competitive edge in the market. To know more about Lowe’s India, visit Lowes.co.in

About The Team Lowe’s forecasting platform team is responsible for predicting future trends, outcomes, or events based on current and historical data. 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.

Job Summary

• The primary purpose of this role is to design, build, and operate scalable software and machine learning solutions that enable Data & AI-driven products. The role combines strong software engineering fundamentals with ML/MLOps, data engineering, cloud-native development, and AI-enabled engineering practices.
• The engineer will contribute across the full Software Development Lifecycle (SDLC), from solution design and development through testing, deployment, production support, and continuous improvement. Key responsibilities include building robust data and feature pipelines, productionizing and scaling ML models, optimizing model training and inference, and implementing model monitoring and lifecycle management.
• The ideal candidate will apply software engineering rigor to ML workloads, leverage AI/ML platforms and automation to accelerate experimentation and deployment, and partner with Data Science, Data Engineering, Product, and Engineering teams to deliver reliable solutions that enable faster, data-driven business decisions.

Roles & Responsibilities

• Design, develop, test, deploy, and support scalable, secure, and maintainable software and ML solutions, including backend services, APIs, cloud-native applications, data integrations, and ML-enabled services supporting Forecasting and AI products.
• Translate business and functional requirements into technical designs and high-quality implementations, contributing across the full Software Development Lifecycle (SDLC).
• Build and maintain data ingestion, transformation, preprocessing, feature engineering, and post-processing pipelines to support ML model training, experimentation, and inference.
• Partner with Data Scientists and Data Engineers to translate model requirements into production-ready engineering solutions and ensure high-quality, reliable data is available for model development and execution.
• Support the end-to-end ML lifecycle, including experimentation, model registration and versioning, testing, deployment, orchestration, inference, and ongoing model management using enterprise AI/ML platforms.
• Develop and maintain CI/CD and automation pipelines for software, data pipelines, and ML models to enable reliable and repeatable testing and deployments.
• Implement monitoring and observability for applications, data pipelines, and ML models, including system health, data quality, model performance and drift, and operational metrics.
• Optimize software, ML workloads, and data pipelines for performance, scalability, reliability, and cost efficiency across cloud and enterprise platforms.
• Apply engineering best practices including secure coding, code reviews, automated testing, documentation, performance optimization…