Information Technology student pursuing a B.Tech (Hons.) and a B.S. in Data Science and Applications, with internship experience in backend development, Python data processing, and web development.
Ments
Jun 2026 – Present
Architected scalable Node.js backend services using asynchronous event-loop concurrency for high-volume concurrent requests; optimized database access with connection pooling and Redis distributed caching, reducing query latency by 40% and improving throughput; integrated secure authentication workflows and optimized RESTful APIs for production environments.
Law & Verdict
Nov 2025 – Feb 2026
Built parallelized Python data-processing pipelines for multi-source legal datasets using multi-threading and batch processing, reducing manual effort by 40%; optimized backend transformation logic across multiple worker nodes, reducing execution time by 30%; implemented validation scripts and automated testing for more than 10,000 daily records.
Paradox IIT Madras
Nov 2024 – Dec 2024 · IIT Madras
Developed reusable React.js and TypeScript frontend components within a team; reduced page load time by 20% through lazy loading, code splitting, and component-based architecture.
B.S., Data Science and Applications
May 2028
Part-Time; CGPA: 7.0
B.Tech (Hons.), Information Technology
May 2027
CGPA: 7.78. Relevant coursework: Distributed Systems, Operating Systems, Computer Networks, DBMS
Intermediate (Class XII)
2021
CBSE; 83%
High school (Class X)
2019
CBSE; 93%
Architected a highly concurrent, fault-tolerant distributed key-value store in Go using goroutines and channels; implemented mutex-based synchronization and consistent hashing for data partitioning; benchmarked read/write QPS and latency under concurrent load.
Built a full-stack Node.js, Express.js, MongoDB, and React.js platform for real-time geospatial travel behavior and trend analysis; optimized location queries with MongoDB aggregation pipelines and 2dsphere indexing.
Engineered an AI-powered semantic search and retrieval-augmented generation platform using Python, LangChain, and ChromaDB; designed low-latency vector indexing and retrieval workflows for large document collections.