Northeastern Library App
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Summary
Redesigned the Northeastern University Library App with a mobile-first approach, focusing on enhancing core user workflows and overall usability through extensive UX research and iterative prototyping.
About
Highly skilled Software Engineer with a Master's in Information Systems, adept at driving efficiency and reliability across full-stack development, automation, and machine learning initiatives. Proven ability to deliver high-impact solutions, from architecting scalable test frameworks that boost execution efficiency by 90% to developing predictive models with 88.5% accuracy. Eager to leverage expertise in robust system design, data analysis, and cross-functional collaboration to innovate and excel in challenging technical environments.
Work
Prodapt Solutions Pvt. Ltd.
|Software Engineer
Chennai, Tamil Nadu, India
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Summary
Led software quality assurance and automation initiatives to enhance application reliability and accelerate delivery cycles for web and mobile platforms.
Highlights
Engineered and maintained a scalable Selenium automation framework using Java and TestNG, boosting test execution efficiency by 90% across web and mobile chatbot applications.
Reduced daily manual regression effort by approximately 2 hours through the development and management of over 1,000 automated test cases for Windows, macOS, Android, and iOS platforms.
Led comprehensive end-to-end functional, regression, and API testing efforts, increasing User Acceptance Testing (UAT) approval rates by 20% and ensuring robust release readiness.
Enhanced backend reliability and performance by executing REST API testing with Postman and performance testing with JMeter, ensuring stable and efficient end-to-end workflows.
Streamlined defect resolution and cross-functional delivery by fostering collaboration with engineers, designers, and product managers, meticulously managing all test artifacts in JIRA and Confluence.
Tech Fortune Technologies
|Machine Learning Intern
Bengaluru, Karnataka, India
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Summary
Developed and evaluated machine learning models to classify data and optimize algorithm performance for data-driven insights.
Highlights
Developed a wine quality classification model utilizing Random Forest classifiers, achieving 88.5% accuracy on a dataset of 1,599 samples with 12 distinct chemical features.
Conducted comparative performance analysis across six machine learning algorithms (Logistic Regression, Decision Trees, Random Forest, KNN, SVM) using Python to identify optimal solutions.
Enhanced model evaluation and interpretability through comprehensive data analysis and visualization, leveraging Pandas, NumPy, Matplotlib, and Seaborn.
Facilitated data-driven model selection by meticulously benchmarking algorithms based on accuracy metrics and performance comparisons.
Education
Northeastern University
Boston, MA, United States of America
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Master of Science
Information Systems
Jyothy Institute of Technology
Bengaluru, Karnataka, India
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Bachelor of Engineering
Information Science & Engineering
Skills
Languages & Databases
Python, Java, HTML, CSS, JavaScript, TypeScript, MySQL, MongoDB, PostgreSQL.
Frameworks & Libraries
Express, Node.js, React, FastAPI.
Testing Tools
Selenium, Appium, Postman, JMeter.
Cloud Platforms
Google Cloud Platform (GCP), AWS, Azure.
Tools & Methodologies
Figma, Balsamiq, Framer, Git, GitHub, GitHub Actions, AWS, GCP, Terraform, Packer.