• Engineered containerized microservices in Golang that process the data with loose coupling, enabled high scalability, and enhanced overall application performance by~2x

• Designed API gateway using Java Spring boot that routes traffic evenly to internal API services, enforcing application security, improved API consumer readability, easing the API usability

Teaching assistant for Introduction to Machine Learning (CIS 662) in the Computer Science department for a class of 100 graduate students.

Collaborated with the professor for the course

• During the Spring semester, under Prof. Natarajan Gautam

• During the Fall semester, under Prof. Chilukuri K. Mohan

• Developed RESTful endpoints that enabled interaction of microservices to form a cluster of nodes using Multicast DNS protocol automatically, improved cluster capacity by 10x, maintaining high availability, exceeding the customer expectations

• Enhanced APIs by leveraging Gorilla Mux and RedFish calls, Integrated with UI, and reduced API response time by 50-60% for a cloud-based solution (GreenLake), resulting in improved user experience

• Led a group of 3 Engineers to develop an internal website to manage the procurement of servers for the data center, which resulted in 10% cost savings to the organization

• Implemented SSL encryption protocol within the microservices cluster, enhancing security and protecting against potential threats, ensuring all communications are 100% encrypted

• Co-authored two technical papers presented at the organization-level tech fest called Parasparam and Tech Con at HPE

• Architected core features for a product (Kubernetes management) launch, resulting in 10+ new enterprise-level customer acquisitions and overwhelmingly positive stakeholder feedback on the product's functionality, performance

• Integrated a notification system that listens to Kafka events/topics for server failures. Sent real-time notifications to customers with low latency, resulting in increased customer adoption

• Collaborated with a cross-functional team, constructed APIs, integrated the backend with the Database, and authored a research paper. Enabled competency-based decision-making, reducing training costs by approximately ~15%

• Devised an SVM-based machine learning model to predict the availability of vulnerability fixes within a specified timeline by combining NVD and ExploitDB data with an accuracy of 93.3%

• Automated the execution of Metasploit exploits and generated the report, saving 20% of the time and effort of engineers