● Integral member of VMware Cloud (VMC) Core platform team responsible for providing a unified data layer for numerous microservices within the SAAS infrastructure.● Delivered a critical component for encrypting sensitive data persisted by microservices that leveragedata fabric.● Developed features like partitioning massive datasets and bulk operations to support complex queries to improve the latency and availability of the database.● Implemented modules to support change data capture (CDC) utilized by microservices that poll and respond to pertinent events.● Primarily responsible for setting up a licensing service that manages multiple activation keys (MAK) forcustomers deploying Microsoft products in VMware-powered SDDCs (Software Defined Data Centers).● Helped develop an AWS Account management system for the deployment of SDDCs and next-generation data storage solutions.

CySIS Lab primarily focuses on exploring dark web to identify cyber-threats in the earliest stages- Responsible for scraping malicious dark web data by developing crawlers and parsers- Involved in effective management and processing of the data mined from dark web- Worked on a machine learning model to predict the Probability of a vulnerability (mentioned in dark web) to be exploited based on the content posted in dark web, description and other metadata pertaining to the vulnerability

• Developed a Continuous Integration test and delivery Pipeline (using Jenkins); the Pipeline had capabilities to build appropriate container images for different types of tests and run the tests in the container• Worked on developing a module to process various directives from a Dockerfile and build OCI container images • Created YAML files to describe and manage multiple Jenkins Jobs using Jenkins Job Builder

• Built a Class based Association Rule Mining algorithm to detect pertinent zero-day threats based on their behavior traces; this enabled researchers, identify different malware outbreaks in the threat landscape• Designed and developed a technique to classify malware into different families by comparing their disassembly code based on n-grams thereby improving the detection rate of the product by 8-10%• Researched on Malware Attribution, which is to mine the human component behind a particular attack and use that data to forecast similar attacks in near future and identify the actors involved• Developed various automations in Python

Designed and developed the Graphical User Interface for the display system and created a Data Simulator for the COMINT (Communication Intelligence) system to suitably display and check the validity of the information received