About

Amit Srivastava is a Senior Software Engineer with 5+ years of experience in designing and developing large scale distributed backend applications. He is currently working at Walmart as a Senior Software Engineer and is responsible for designing, developing and implementing large-scale distributed systems that process a large volume of data. His focus is on scalability, latency, efficiency and fault-tolerance for end-to-end delivery. He works with architects in technical design discussions to find optimal solutions to critical problems and ensure agile product delivery. Amit has extensive experience in designing and developing services using micro-service architecture and focuses on event-driven systems to ensure de-coupling of components. He has a strong focus on system scalability and has designed & developed an Alarm Generation Engine to detect anomalies in Walmart refrigeration systems. He has also designed & implemented complex business rules using Azure Stream Analytics for near real-time fault detection in edge devices. Additionally, he has designed and developed an automatic Work-order generation system using Azure functions. Amit has worked on RBAC implementation to control role-based access to application features and has established a Disaster Recovery pipeline for cloud components to create a fail-safe and resilient system. He has also implemented caching mechanisms using Azure Redis to enrich incoming telemetry with meta-data. Amit has led multiple features and technical optimizations to ensure on-time delivery to customers. He has raised the bar on code quality, documentation, testing, debugging, and troubleshooting. Amit has a Bachelor's degree in Information Technology from VIT University, Vellore. He has experience working with Azure, Redis, Microservices, Kubernetes, Databricks, dotNET, Frontend, Backend, Docker, ASP.NET, Redux, and Parts-of-speech. Amit has relevant experience of 5.85 years and has worked with Walmart as a Software Engineer III and Software Engineer II in the past. He has worked on ASA Auto-scaler to save around 50% of cloud cost on ASAs and improved performance by up-to 30%, cost by 60%, and responsiveness by 50% for Line of Business (LOB) applications hosted in Azure Kubernetes services by utilizing Horizontal Pod Autoscaling (HPA).