• Developed and deployed payment monitoring dashboards for payments processed through multiple channels, utilizing technologies such as Java and ReactJS to generate actionable business insights and provide granular visibility into the status of approximately 2 million daily transactions• Designed and implemented a performance testing suite using Blazemeter to simulate real-world scenarios, achieving a 1500 transactions-per-second throughput for the monitoring system, optimizing system performance and scalability• Engineered a robust data synchronization process between primary and monitoring databases using scheduled jobs and microservice APIs, ensuring real-time data consistency and accuracy for critical payment data• Developed a microservice for a modernized system that consumed messages from the databus using Apache Kafka. This involved ensuring seamless progression through various deployment stages using a CI/CD pipeline and Kubernetes, thereby maintaining system efficiency without disrupting the application• Contributed to the development of OTC financial swaps software, automating key workflows from trade capture to settlement. Implemented rigorous validation and compliance checks, reducing operational risk and enhancing runtime efficiency by 30%

Personal tutor to students for SAT Math and EBRW sections

• Built a regression tool using Beyond Compare to automatically compare outputs from different code versions, speeding up debugging and reducing development time for the team• Migrated logging functionality of a middle office service application from Log4j to Logback using a PPA ingestion scheme, ensuring seamless log retention and performance, as recommended by the central technology team

Grader for the course CSC 505 - Design and Analysis of Algorithms under Prof. Jamie Jennings

Personal Tutor to 5 students studying the course PHY 211 at NCSU under the Global Training Initiative

• Developed and optimized a data pipeline for PyrAmes Inc. to convert raw medical sensor data into usable blood pressure waveforms. Implemented robust preprocessing and noise reduction techniques using Python, reducing manual labor and improving data quality• Conducted in-depth analysis of generated data to identify and select the optimal neural network model for integration employing parameters like runtime, accuracy, precision, etc. to evaluate model performance and align with product requirements• Created comprehensive visualizations of model statistics and metrics adhering to FDA guidelines to effectively communicate complex data and support regulatory approval

• Investigated statistical distributions of data anomalies collaborating with Bryte’s Algorithms team, identifying patterns and proposing new threshold values to improve anomaly detection, thereby enhancing the system’s reliability• Developed a data validation tool for blood pressure values, ensuring the robustness of the data pipeline for PyrAmes. Optimized data storage by transitioning from CSV to HDF format, achieving a 5-6x compression ratio. Developed high-performance reader/writer components in C and Cython to accelerate data processing

Developed a common landing and login page for the internal portals of the company. Performed research and analysis of Application Performance Monitoring and Business Transaction Monitoring tools to facilitate the decision-making process of the Technology Department and help reduce Mean Time to Resolve (MTTR) of technical issues by 40-45%

Served as the Teaching Assistant for Data Structures course to teach the basic core concepts of the subject to 45 academically weak students of freshman year under the 'Study Hours' initiative by the university

Implemented PLC technique analysis of M3 data per pixel basis to generate images showing EBCs and EBDs which help in further analysis of minerals at the specific locations.