Software Engineer

Innovate in Bengaluru

This position is based at our on-site office in Bengaluru. Lowes 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 Lowes:

Lowes is a FORTUNEregistered 100 home improvement company serving approximately 16 million customer transactions a week in the United States. With total fiscal year 2024 sales of more than $83 billion, Lowes operates over 1,700 home improvement stores and employs approximately 300,000 associates. Based in Mooresville, N.C., Lowes 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.

About Lowes India:

Lowes India, the Global Capability Center of Lowes Companies Inc., is a hub for driving our technology, business, analytics, and shared services strategy. Based in Bengaluru with over 4,500 associates, it powers innovations across omnichannel retail, AI/ML, enterprise architecture, supply chain, and customer experience. From supporting and launching homegrown solutions to fostering innovation through its Catalyze platform, Lowes India plays a pivotal role in transforming home improvement retail while upholding strong commitment to social impact and sustainability. For more information, visit Lowes India

About The Team:

Lowes Demand Forecasting team, commonly known through its Lowes Internal Forecast Tool (LIFT), develops and operates data-driven forecasting capabilities that improve demand-planning accuracy and reduce manual intervention. The team applies time-series analysis and AI/ML models to support supply-chain efficiency, inventory optimization, and fulfillment decisions. Its forecasts span weekly item-location demand across Lowes stores, store-level sales for labor planning, new-store sales estimates, truck arrivals, and transportation shipment volumes. The teams work is central to inventory ordering, allocation, replenishment, and exception management. By incorporating trends, seasonality, and market intelligence, it creates demand and sales forecasts that support planning partners across supply chain, merchandising, inventory fulfillment, and transportation.

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.

Key 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, data governance, security, and compliance.
• Support production systems through monitoring, incident resolution, root-cause analysis, troubleshooting, and continuous reliability improvements.
• Collaborate with Product, Data Science, Data Engineering, Architecture, Security, Infrastructure, and business teams to deliver reliable Forecasting and AI capabilities that support business decision-making.
• Participate in technical design discussions and code reviews, contribute to engineering standards, share knowledge with team members, and evaluate modern technologies, AI-enabled development tools, and automation to continuously improve software quality and engineering productivity.
  Required
• EDUCATION QUALIFICATIONS
• Bachelors degree in computer science, computer information systems, or related field or equivalent years of experience in lieu of education requirement, if applicable

Preferred

Masters degreein computer science, CIS, or related field

Required

Work Experience

• 2 years of experience in software development or a related field
• 2 years of experience working on project(s) involving the implementation of solutions applying development life cycles (SDLC) through iterative agile development
• 2 years experience working with any of the following: frontend technologies (user interface/user experience), middleware (microservices and application programming interfaces), database technologies, or DevOps.
• Minimum 2 years of experience in applications powered by AI/ML with large dataset and Data Engineering.
• 2 years experience working with any of the following: frontend technologies (user interface/user experience), middleware (microservices and application programming interfaces), database technologies, or DevOps.
• Minimum 2 years of experience in applications powered by AI/ML with large dataset and Data Engineering.
• Hand-on experience (real-time) & proficiency in building robust, reliable & scalable data/ML pipelines for Analytics & AI/ML systems, working with large sets of structured and unstructured data from disparate sources.
• MLOps tools and frameworks 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 systems - Hadoop Ecosystem (HDFS, Hive, MapReduce) or Cloud
• Analytics database like Druid, Data visualisation/exploration tools like Superset.
• CICD
• GIT

• 2 years of experience writing technical documentation in a software environment and developing and implementing business systems within an organization
• 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.