Bachelor of Engineering - BE

Master of Science - MS

Developing dynamic Frontend pages using React, Bootstrap, and React-Bootstrap Libraries.
• Utilizing Redux-Toolkit for state management.
• Integrated frontend with razorpay instances.

Software Developer

•  Resolved TypeScript linting issues to ensure code quality.
•  Built an Admin Panel in Next.js with TypeScript for managing notifications.
•  Integrated WhatsApp and Twilio APIs in a NestJS backend for automated messaging.
•  Optimized notification workflows with error handling, logging, and bulk processing.

Full-Stack Intern

•  Developed a notification service using AWS Lambda, SQS, and SES with Node.js, enabling asynchronous message processing and efficient delivery of emails with dynamic templates.
•  Designed, developed, and integrated microservices with AWS Lambda using the Serverless Framework and Node.js, deploying scalable, event-driven architectures with AWS services such as API Gateway, MongoDB, and S3.
•  Optimized performance and reduced operational costs by leveraging serverless best practices.
•  Monitored CI/CD pipelines for automated deployment.
•  Ensured security and compliance by managing IAM roles, API authentication, and environment variables.

•	Researched 10+ websites to develop and implement key product features, enhancing the Meeting Scheduling System with seamless integrations.
•	Designed and Developed backend architecture for Meeting Scheduling System from scratch including Razorpay Payment Gateway with Express server and MongoDB database.
•	Implemented Razorpay Payment Gateway integration for payments and payouts to the Mentors using razorpay APIs.
•	Integrated Google Calender APIs using Oauth 2.0 for scheduling video meetings.
•	Developed modular and dynamic frontend dashboard of user and mentor with ReactJS and Material UI

Von Mises Stress Prediction using ML & DL 
Thesis Project under Dr. Rajiv Sharma | Design and Simulation Lab, Ocean Engg Department| IIT Madras

• Submarine Hull has hollow cylindrical structure and Von-Mises stress depends on radius, length, thickness, and operable depth and various other parameters.
• Objective of using ML and DL is to find the correlation between these parameters and find the feature importance.
• Von-Mises stress is predicted using polynomial regression, Decision Trees, Random Forest, KNN, and Neural Network and got 99 percent accuracy.
• By analyzing the results it concluded that radius, length, thickness, and operable depth of hull is primary and important feature.

Research Scholar

1. Developed a dynamic seat reservation module using React with state management.
2. Built backend services with Express.js and integrated MongoDB for database management.

• Integrated external APIs using FastAPI for the backend with Postgres SQL and Next.js for the frontend across two US client projects.
• Engineered data extraction pipelines using prompt engineering and Pandas for processing Excel sheets.
• Designed and optimized backend calculation logic, handling edge cases using a custom JSON hash map, improving accuracy by 50%
and reducing latency.
• Deployed the solution in a microservices architecture, ensuring scalability and performance.
Implemented feature testing with pytest.

Software Engineer

developer

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