About
Computer Science and Engineering student at NIT Srinagar with experience in backend development, full-stack web applications, and AI/RAG systems. Built projects using modern web, cloud, and data infrastructure across real-time messaging, interview prep, and document intelligence workflows.
Experience
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Backend Member, Web Development TeamRang-e-Chinar, NIT SrinagarSep 2025 – Oct 2025
Collaborated with a development team to implement server-side workflows and backend request handling for the college fest website. Integrated REST APIs and JSON handling to connect frontend features with backend services.
Education
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2028
B.Tech.Computer Science and Engineering (CSE) · Aug 2024 – May 2028
Skills
Tools / apps / platforms
Projects
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AI Interview Platform – Full-Stack Interview Preparation SystemReact, Node.js, Express.js, MongoDB, Firebase Auth, OpenRouter, Razorpay
Developed a full-stack interview preparation platform with resume-based candidate profiling, speech recognition, text-to-speech, LLM-based answer evaluation, analytics, downloadable PDF reports, and premium payment workflows.
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Enterprise Agentic RAG – Document Intelligence PlatformPython, FastAPI, Streamlit, LangGraph, OpenAI, BGE-M3, Qdrant, BM25, RRF, Tavily
Built a hybrid document Q&A pipeline with dense and lexical retrieval, reranking, and corrective RAG workflows. Validated performance with RAGAS benchmark results and evidence-grounded generation.
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Distributed Real-Time Messaging PlatformNode.js, Express.js, MongoDB, Redis, RabbitMQ, Socket.IO, Docker, AWS EC2
Engineered a 3-service backend for authentication, chat, and email, decoupling asynchronous OTP dispatch via RabbitMQ and implementing strict Redis rate-limiting. Architected secure MongoDB chat APIs and implemented real-time messaging, presence, typing, and read-state features via Socket.IO.
🏆 Achievements & awards
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Smart India Hackathon 2026 (PS 26186) · 2026
Advanced past the internal round at NIT Srinagar among 80+ competing teams; engineered the SURAKSHAI backend using Flask and MongoDB, and integrated an XGBoost-based risk prediction model with SHAP explanations for transparent wellness tracking.
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LeetCode Peak Contest Rating
Achieved a peak contest rating of 1,529 and solved nearly 350 problems of Data Structures and Algorithms.