About

Babish Adapa is a highly skilled and experienced senior software engineer at Microsoft with over 8 years of relevant experience. He is a results-driven, customer-focused, articulate, and analytical professional who can think "out of the box". Babish is an expert in low-latency, high-throughput, scalable distributed system design and implementation. He possesses strong written and verbal communication skills and is interested in a challenging technical/management track career in an application development environment. Babish has real-world work experience in the latest cutting-edge technologies, including Microsoft Azure Compute, Angular JS, Micro Services, ASP.NET MVC, Web API, and managing high security for applications. He is currently creating the core search provider - Navigational search for Purview and has also created features to manage Sensitivity labels applied to Outlook, Teams, SharePoint, and other office suite applications. In his previous role at Microsoft as a Software Engineer 2, Babish developed core queuing and routing algorithms for a large-scale multi-data center aware distributed software system, which is the integral part of one of the products ROQ MultiLane. His work enabled Microsoft to save approximately $2M for every subsequent product release. Babish also worked on priority-based process flow algorithms, automation algorithms that integrate with large products like Windows and Office bits/process pipeline, and leveraged analytics on telemetry data for making decisions on customer experience, infrastructure, app simplification, and feature retirement. Babish has a Master of Business Administration degree from BITS Pilani, Hyderabad Campus, with a field of study in Business. He possesses a diverse tech stack, including Software Engineering, Javascript, AngularJS, test, Search, dotNET, ReactJS, ASP.NET, Microservices, Azure, Web, and infra. Babish is passionate about learning emerging industry technologies, processes, and best practices. He has improved cycle time performance for Windows from 4 hours to 30 minutes by simplifying the existing solution to easily maintainable/scalable for future volumes.