• University: Northeastern University
• College: NA
• Major: Data Science
• Minors: NA
• Start Date: 05 September 2023

• University: BMS College of Engineering
• College: NA
• Major: Computer Engineering
• Minors: NA
• Start Date: 14 August 2018

AI Software Engineer Co-op

Developed ETL pipelines in R and Python, leveraging AWS EC2 and S3 for efficient processing of large healthcare and gene expression datasets (100M+ records), improving data ingestion and preparation times by 30%. Designed scalable RESTful APIs using AWS API Gateway. Performed gene-level analysis on Seurat objects, calculating expression for 20,000+ genes, filtering datasets based on gene criteria to visualize distinct clusters and cellular identities. Implemented interactive visualizations using React, TypeScript, plotly, and D3.js, handling over 1 million data points with worker threads and streaming, reducing client-side rendering time by 40%.

Software Engineer

Developed scalable RESTful-APIs for enterprise-grade microservices using Spring Boot and implemented business logic for Oracle Banking application, adhering to the 12-factor app methodology, resulting in a 30% increase in performance. Architected application to handle high-throughput real-time data streams for user-centric dashboards, significantly improving the delivery of actionable insights. Engineered end-to-end data pipelines, integrating big data tools with SQL and Java-based ETL processes to transform and load real-time financial datasets into analytics dashboards, ensuring consistent data accuracy and reliability. Designed and maintained automated CI/CD workflows with Jenkins and Gradle, integrating containerized applications with OCI cloud environments, which enhanced deployment efficiency by 40% and minimized downtime in production systems.

Machine Learning Intern

Designed and implemented a data preprocessing pipeline to clean and transform raw data, reducing data cleaning time by 50% and improving data quality for supervised machine learning models. Applied supervised machine learning techniques to predict tenure using user churn and features (Customer Segmentation) with 96% accuracy, leading to substantial cost savings and differentiation in customer offerings.

• LGBTQ: no
• Ethnicity: Asian
• Disability: no
• Veteran: no
• First-generation College Student: yes
• Pell Grant Eligibility: no
• Work Authorization: yes
• Visa Sponsorship: no