Currently working as full stack developer playing around with grapqhl, java, typescript, nextjs, spring boot
Software Engineer
Summary
Cut template download latency from 2s to 500ms using Redis caching and parallel Java service calls Written modular microservices services improving code scalability and maintainability Used: Java, Graphql, Python, Nextjs, Typescript
Software Engineer Intern
Summary
Developed Frontend on Supplier Product Addition tool Written Test Cases for Frontend and also developed api using Java Used: TypeScript, NextJs, Java, GraphQl, Docker
Pr Co-Coordinator
Summary
-Spearheaded the successful activities at Incubation Centre NIT Patna, -Showcasing dedication and commitment
Discipline Lead
Summary
-Demonstrated exceptional leadership skills as the Discipline Lead for Hackslash -Driving innovation and fostering a collaborative environment
B.tech
Computer Science and Engineering
Grade: 8.29
Board of Intermediate Education
Grade: 87.8
Maximum 1407 rating
Got under top 3.1% of global top Leetcoder with rating 1960+
Achieved a competitive ranking of under 8500 on leetcode
Achieved rating of 1808, showcasing exceptional skill and dedication
C, C++, Python, JavaScript, Java, MySql, HTML, CSS, PI/Sql.
Djnago, ReactJS, NodeJS, ExpressJS, Jquery, Mongodb, TailWind CSS, NoSql databases.
Git, Github, Linux, Windows, Amazon Web Services (AWS).
Team management, Leadership, Event Management, Public Speaking.
Web Development, Blockchain, Competitive Programming, Data Structures and Algorithms.
Object-Oriented Programming(OOPs), Operating Systems, Computer Networks, RDBMS, Networks, Network Programming, Database Management Systems(DBMS).
Summary
Made a platform that allows users to post, delete, and comment on ideas Tools & technologies used: JavaScript, HTML, CSS, MongoDB, Redis, Node.js, Express.js, React.js, Socket.js, Google auth, Amazon AWS Platform where users can post, comment and like each other Post, comments Implemented features for users to like posts and comments Integrated real-time chat functionality for seamless communication and collaboration among users
Summary
Developed a cutting-edge LSTM model to classify text as Hate speech or not, achieving an accuracy of 92% Tools & technologies used: Machine Learning, Deep Learning, Google Colab, Python Utilized GloVe and BERT for vectorization and achieved state-of-the-art performance