Shivam garg

Designed and enhanced backend services for warehouse automation by optimizing the HTM Bot Workflow, improving task execution efficiency and ensuring reliable processing for large-scale robotic operations. Led the architecture and development of a distributed Bot Relay Framework, enabling reliable communication, task scheduling and execution across 200+ autonomous warehouse robots deployed in multiple customer warehouses. Accelerated engineering delivery by resolving 200+ production backlog issues within one month using Claude AI for intelligent code assistance, reducing technical debt and improving overall software maintainability. Improved production observability by implementing performance monitoring with Prometheus and Grafana, proactively identifying system bottlenecks and increasing platform stability.

Designed and developed backend services for India's largest online bus inventory platform, supporting search, inventory management, and booking workflows. Optimized distributed backend systems using Redis, AWS S3, and scalable architecture, improving operational efficiency by 30%. Led end-to-end feature development, from initial to product launch. Notably developed Nearby Search which improved search accuracy by 40%. Designed and optimized a high-performance search engine serving approximately 10K requests per minute, reducing search latency from nearly 100 ms to ~3 ms while increasing search relevance by 50%. Designed and implemented an Inventory Backup System to ensure high availability and business continuity by maintaining reliable backup data for recovery during system failures, minimizing downtime. Participated in architecture discussions and contributed to the design of scalable, fault-tolerant microservices capable of handling increasing business traffic. Collaborated with cross-functional teams including Product, QA and DevOps to deliver production-ready features while maintaining high code quality and timely releases.

Developed a Service Assistant Chatbot using Flask, Microsoft Bot Framework, and Azure Cognitive Services (LUIS/CLU) for intent recognition and entity extraction to enhance customer experience and optimize business operations. Developed Spring Boot REST APIs with MySQL and MongoDB aggregation for an enterprise Admin Dashboard, enabling daily/monthly usage analytics across country, language, and use case dimensions. Improved dashboard performance by 95% by applying MongoDB indexing, reducing response times from 23 minutes to 510 seconds, resulting in a highly efficient and responsive system.