BTech Undergrad | Software Development Engineer | Cloud & Backend Specialist

I am a Computer Science engineering student focused on building highly scalable, data-driven systems and robust backend infrastructure. With professional experience as a Flutter developer and deep competency in Google Cloud Platform (GCP), I build software that solves complex problems without compromising performance.

Technical Highlights:

• AI & Data Mining: Proven ability to engineer predictive time-series pipelines and integrate Generative AI (LLMs) into production workflows, recently placing Top 5 Nationally at the UIDAI Data Hackathon.
• Core Engineering: Strong command of Java, Python, and C++ with a strict focus on Data Structures & Algorithms. I possess a deep understanding of low-level computing, demonstrated by engineering a custom LC-3 Virtual Machine in C.
• Systems Architecture: 4x National Hackathon Winner. Experienced in designing distributed architectures, efficient caching layers (Redis), and lightweight orchestration APIs (Hono).

I am looking to bring this technical rigor to a Software Engineering Internship at a product-focused company that values problem-solving and architectural depth.

• Languages: Java (Proficient), Python, C++, C, TypeScript, JavaScript, Dart, Solidity, SQL
• Frameworks & AI: PyTorch, React, Node.js, Hono, Flutter, Scikit-Learn
• Cloud & Databases: Google Cloud Platform (GCP), Redis, MongoDB, Docker, Azure
• Core Concepts: Data Structures & Algorithms, Distributed Systems, System Architecture, OOP
• Tools: Git/GitHub, Linux (Shell Scripting), Postman

Top 5 National Finalist - UIDAI Data Hackathon 2026 A predictive AI pipeline to map service deserts and forecast compliance demand.

• The Tech: Python, PyTorch, Scikit-Learn, Pandas, Folium
• Key Challenge: Ingesting and processing 5M+ records across 1,070 districts, and optimizing a time-series LSTM model to reduce forecasting error.
• Outcome: Achieved a 1.7% RMSE reduction over baseline models and successfully mapped high-risk zones using K-Means clustering and SHAP explainability.

Reddit Mod Tools Hackathon '26 A full-stack, AI-powered moderation assistant and unified queue orchestration API.

• The Tech: React 19, Hono, TypeScript, Gemini 1.5 Flash, Redis
• Key Challenge: Managing LLM latency and building robust fallback heuristics for real-time, high-volume moderation queues.
• Outcome: Architected a highly performant, cache-efficient API using Redis and Set-based deduplication to instantly surface automated decisions and historical precedent.

Systems Architecture Simulator A low-level simulation of the LC-3 computer architecture written in C.

• The Tech: C, Makefile, Unix Systems, POSIX
• Key Challenge: Implementing the full instruction cycle (Fetch, Decode, Execute) and memory-mapped I/O, requiring precise management of bitwise operations, sign extension, and condition flags.
• Outcome: Successfully simulates trap routines and deterministic opcode processing via a custom 64K memory array, demonstrating core systems-level programming capabilities.

• 🏆 Top 5 National Finalist | UIDAI Data Hackathon 2026
• 🥇 Winning Project (Web3 Track) | HackASol 4.0
• 🚀 National Qualifier | NASA Space Apps Challenge (2024 & 2025)
• ⭐ Top 25 Team (Monad Track) | HackHazards 2025