🎯 Built a scalable fanout service serving as a backbone for multi-entity workflows and downstream integrations.

🎯 Developed the Mass Pay feature, enabling customers to pay up to 2,000 bills per batch, orchestrating over $100M in transaction volume (TPV) across multiple child entities within six months.

🎯 Designed a devEx agentic tool to generate API schema and data-processor changes, boosting productivity by 60%.

🎯 Developed and refactored APIs for account-payable operations, optimizing performance and throughput.

⚡⚡ Course Producer for the following courses:-

1. CSCI 572 - Information Retrieval and Web Search Engines with Prof Saty.

2. DSCI 552 - Machine Learning for Data Science with Prof Rajati.

⚙️⚙️ Responsibilities:

🎯 Assisting students by addressing questions and doubts through Piazza.

🎯 Conducting regular office hours.

🎯 Preparing rubrics for assignments and exams.

🎯 Grading exams and assignments.

🎯 Developed an order delivery orchestration product capable of handling up to 100,000 orders per hour.

🎯 Led the seamless migration of a legacy system from Informatica ActiveVOS to a RESTful web application deployed in dockerized containers on Kubernetes clusters, achieving a 60% reduction in operational costs.

🎯 Architected 10% reduction in API response time by implementing security using API Gateway.

🎯 Reduced response time of async events by 15% by redesigning back-end logic of notification handlers.

🎯 Orchestrated resolution of development and infrastructure failures across production servers, conducting comprehensive failure analyses and executing corrective measures to ensure optimal system performance.

🎯 Created automation scripts to help check environment sanity and expedite regression testing by 25%.

🎯 Developed comprehensive unit tests of software modules to ensure maintainability and reliability.

🎯 Reduced costs by optimizing developer environments, which reduced the team’s cluster requirement by 40%.

🎯 Worked under Dr. P. S. Rana at Thapar Institute of Engineering and Technology on audio genre classification using machine learning techniques.

🎯 Developed a novel methodology for music genre classification in the domain of Music Information Retrieval (MIR).

🎯 Extracted distinctive feature vectors from audio signals and applied various machine learning algorithms to optimize genre classification.

🎯 Introduced an ensemble learning approach to augment classification accuracy, yielding an average precision of 85%.

🎯 Employed K-Fold cross-validation to assess the robustness and consistency of the ensemble model.

🎯 Validated the efficacy of the ensemble method using Topsis analysis, achieving a robust score of 0.97.