- M.S., Computer Science | North Carolina State University (August 2023 - May 2025)
- B.S., Electronics and Communication Engineering | CVR College of Engineering (August 2018 - May 2022_)
Software Engineer @ LexisNexis (June 2025 - Present)
- Built and maintained backend REST APIs for core product features with a focus on reliability and production stability.
- Migrated legacy search systems to a cloud-native, microservices architecture, improving scalability and reducing operational cost.
- Refactored services and improved data access for large-scale document search workloads.
- Implemented CI/CD pipelines and safer deployment practices (automated testing, dark launches) to reduce risk during releases.
- Worked closely with cloud infrastructure (AWS) and monitoring to debug performance and production issues.
AI/ML Research Assistant @ NC State (January 2025 - May 2025)
- Developed a Python- and MySQL-based pipeline to evaluate and improve the quality of crowd-labeled datasets.
- Integrated label quality metrics into an existing ML workflow on the Expertiza platform.
- Worked on semi-supervised label refinement and quality-aware calibration in human-in-the-loop learning systems.
- Contributed to research exploring how data reliability impacts downstream model performance.
Software Developer @ Valuelabs (April 2022 - July 2023)
- Developed Java and Spring Boot microservices within Order Management Systems (OMS) to automate the end- to-end process of yearbook orders for 50,000+ schools.
- Streamlined API development with Spring Boot’s modularity, enhancing scalability and integration.
- Implemented Scheduler using Jenkins to automate technical issues notifications, significantly boosting system efficiency. This strategic deployment of schedulers resulted in a 15% improvement in resolving issues faster.
- Utilized OracleDB to modify and update task specific errors and address data-specific issues reported by over 10,000 end users on day-to-day basis. This process streamlined the
validation of downstream calls, ensuring efficient and rapid request-response cycles. - Monitored logs using Workload Manager to check the flow of the data across different teams and debugging processes.
Software Development Intern @ Valuelabs (January 2022 - April 2022)
- Performed unit testing automation using JUnit and performance testing using JMeter, resulting in a notable code coverage increase of 92%. Developed exceptional work ethics, time management skills, and teamwork, gaining praise and a full-time position.
Software Intern @ OpenText (November 2021 - January 2022)
- Gained hands-on experience in software development, where I learned industry best practices and improved my skills in version control using Git, was also introduced to an Agile environment, enhancing my ability to work efficiently within collaborative, iterative development cycles.
Paper Title: WIP: Quality-Control Metrics for Crowdsourced Labeling: Promoting Dataset Reliability for Machine- Learning Applications.
Paper Submission Link - 1571119664
The paper focuses on defining and evaluating quality-control metrics for crowd-labeled datasets used in machine learning. It examines how inconsistencies and unreliable annotations affect downstream model performance and proposes metrics to identify low-quality labels. The work integrates these metrics into a human-in-the-loop learning pipeline, enabling semi-supervised label refinement without requiring full re-annotation. The core contribution is demonstrating that explicitly modeling label quality can improve dataset reliability and lead to more robust ML systems.
Developed a backend framework to scrape and aggregate best deals from major e-commerce websites (Walmart, Target, BestBuy, eBay). Designed and implemented robust API endpoints using FastAPI. Integrated PostgreSQL for efficient data management and storage. Optimized search performance, reducing query time by 50%. Collaborated in an agile environment, contributing to the project’s successful deployment.
Developed SimplyClip, a browser extension that enhances productivity by managing multiple text snippets across tabs. It allows users to select, merge, and sort snippets, set reminders, save URLs, and export content to CSV or DOC, with features like dark mode. Utilized JavaScript for core interactions and clipboard management, Python with Django for backend API, and HTML/CSS for a responsive interface. Implemented Node.js for server logic and used NPM for dependency management, employing ESLint and Prettier to ensure code quality.
Designed a flexible cache and memory hierarchy simulator in C++ to evaluate performance, area, and energy efficiency across configurations. Developed L1/L2 cache modules and implemented a prefetcher using Stream Buffers, successfully running over 100,000 read and write operations for performance assessment. Streamlined execution with a Makefile for multi- instance cache interactions.
Worked on data preprocessing and detailed exploratory data analysis. Merged with enrichment data to understand various correlating features and drafted initial Hypothesis. Implemented Machine learning and statistical models to predict the trend of COVID-19 cases and deaths, which supports initial hypothesis and improved overall F1 by 5% than baseline evaluations. Built a user-friendly dashboard to display results utilizing Dash framework.
Used MongoDB to centralize driver info, permit records, parking availability, and citations. Implemented efficient data processing and reporting for enhanced parking management and violation tracking.