Apr 2022 – Sep 2024 · 2 yrs 5 mos · Bangalore Urban, Karnataka, India · On-site

• ◦ Created shipment widget for tracking new debit cards using React.js and delivered it within 3 days as part of a hackathon, resulting in significant cost and time savings.
• ◦ Created ‘Activate with CVV’ screen on PayPal Debit Card Web in React.JS, and performed A/B testing for US and EU rollouts on 10% sample.
• ◦ Designed and implemented Open Banking using GraphQL schema and Java Spring Boot, serving 700+ daily customers in the US and EMEA.
• ◦ Architected Low-Level and High-Level Design, implemented the Accept API design for the Term Loan
• service within Credit Card team, and conducted rigorous unit testing to address performance bottlenecks.
• ◦ Worked on JDBC transaction handling in Spring Data JPA to perform CRUD operations.

Jul 2021 – Apr 2022 · 9 mos · Bangalore Urban, Karnataka, India · On-site

• ◦ Built and maintained microservices that consumed and processed 100 requests every minute from the Paypal Working Capital’s website using Springboot.
• ◦ Replaced the legacy dependencies in functional tests with mock tests resulted in a stable build, reduced the build time from 18 minutes to 15 minutes, and FT running time from 11 minutes to 8 minutes.
• ◦ Triaged production alerts through observability tools like Splunk, monitored system health and performance, and designed dashboards to pinpoint the root causes of 5xx errors, SSL handshake timeouts, and forbidden errors in downstream APIs. Achieved substantial error reduction up to 65% while improving error detection efficiency.

Aug 2020 – Jun 2021 · 10 mos · Bengaluru, Karnataka, India

• ◦ Developed microservice using SpringBoot and React.js, enabling daily probes for detecting anomalous behavior in SQL queries by the Risk Team.
• ◦ Added feature to export the stress analysis reports that made tracking validation breaks easy, saved 10
• clicks/user.

Jan 2020 – Aug 2020 · 7 mos · Bengaluru, Karnataka, India

• Worked on automating Outlier Detection in Risk Financial Dataset using Supervised Machine Learning algorithms namely Random Forest, Logistic Regression and Support Vector Machine. Achieved 94% accuracy.

Jun 2018 – Jun 2018 · 0 mo · India

• I got the opportunity to work as a research intern at Bennett University, in collaboration with NVIDIA, to create an OCR under the guidance of Dr. Deepak Garg. My solution aimed at converting English text in images into French speech using deep learning. The process involved image preprocessing, segmentation of words into characters, character recognition using text synthesis, and language translation. For character recognition, I created a Convoluted Neural Network model that I trained on the Chars74K dataset. For language translation, I used Recurrent Neural Networks and the seq2seq model.