Software Engineering Student | Cloud Architect | AI Enthusiast Building scalable, resilient systems with a focus on Cloud Infrastructure, AI Security, and Behavioral Analytics.
- 🎓 Education: Pursuing B.E. in Computer Science (Expected 2027) | 9.09 CGPA
- ☁️ Expertise: AWS Certified Cloud Intern with experience in Infrastructure Automation & Migration.
- 🤖 Current Focus: Building production-grade, security-hardened RAG / Generative AI systems (LangChain, Guardrails, Red-Teaming).
- 🏛️ Philosophy: Driven by an AI-native curiosity to spot normalized problems and build scalable solutions for a better, more secure Internet.
- Languages:
Java,Python,TypeScript,SQL (PostgreSQL),C++ - Cloud & DevOps:
AWS (EC2, S3, RDS, Lambda),OCI,Modal,Docker,CI/CD Pipelines (GitHub Actions) - Frameworks:
Next.js,FastAPI,Streamlit,Electron,Node.js - AI / ML:
LangChain,ChromaDB,PGVector,Groq,Model Context Protocol (MCP),XGBoost - IoT:
ESP32,Arduino
A production-grade, security-hardened RAG agent for enterprise support. Persistent Supabase/PGVector retrieval, serverless deployment on Modal, sub-second inference via Groq (Llama 3.3-70B), and a Zero-Trust Dual-Scan Guardrail layer that blocks prompt injections, jailbreaks, and PII leaks in real time.
A full-stack meeting scheduler ensuring 99.9% service availability. Built with React, TypeScript, and Supabase.
High-performance behavioral analytics tool using C++ Win32 kernel hooks to capture 120Hz telemetry with <1% CPU overhead.
ML engine using XGBoost and 7-day lag autoregression (
$R^2 \approx 0.31$ ) for city-scale congestion forecasting.
Real-time IoT water quality monitor built on ESP32, streaming TDS & Turbidity telemetry to ThingSpeak with automated Twilio SMS alerts when readings cross safe thresholds.
- 🐦 X / Twitter
"The best way to predict the future is to invent it."