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upasanaprabhakar/README.md

MasterHead

Hi πŸ‘‹, I'm Upasana Prabhakar

πŸš€ Full-Stack Developer & AI Systems Builder | Top 7 @ HackFest 2 among 500+ | AI/ML Intern @ Infosys | Shipping Production-Grade Apps


  • πŸ”­ I'm currently working on CareerTrack β€” a full-stack job application tracker with AI-powered resume-to-JD gap analysis using Groq LLM

  • πŸ† Achieved Top 7 at HackFest 2, GDG Cloud New Delhi among 500+ participants with DataSense AI β€” a multi-agent data dictionary generator

  • 🌱 I'm currently learning Advanced System Design Β· LLM Orchestration & Multi-Agent Pipelines Β· PostgreSQL & Supabase at scale

  • πŸ’Ό Past experience: AI/ML Intern @ Infosys Springboard β€” built production-grade NIDS using ensemble ML models (Decision Trees, Random Forests, Gradient Boosting)

  • πŸ‘― I'm looking to collaborate on Notes-App

  • πŸ“ I regularly write articles on medium.com/@upasanaprabhakar35

  • πŸ’¬ Ask me about MERN Stack, Full-Stack Architecture, Multi-Agent AI Systems, REST APIs, ML Engineering

  • πŸ“« How to reach me: upasanaprabhakar35@gmail.com


πŸš€ Featured Projects

React Β· Node.js Β· PostgreSQL Β· Prisma Β· Socket.io Β· Groq LLM Β· JWT Auth

  • Built and deployed a production-grade job tracking system with 7-stage pipelines, real-time notifications via Socket.io, and AI-powered resume-to-JD gap analysis using Groq LLM
  • Reduced manual interview prep time by 60% through role-specific AI suggestions and keyword gap detection
  • Implemented JWT auth with httpOnly refresh token rotation, rate limiting, and CORS across Vercel + Render + Neon PostgreSQL

React Β· Node.js Β· Groq (Llama 3.3 70B) Β· Supabase Β· D3.js

  • Built a 3-agent pipeline (Atlas β†’ Sage β†’ Guardian) that turns any undocumented CSV or database into a fully documented data dictionary in under 30 seconds
  • Guardian auto-scores data quality (0–100) and detects PII across 11 categories with GDPR risk classification
  • πŸ† Top 7 at HackFest 2, GDG Cloud New Delhi among 500+ participants

Python Β· Scikit-learn Β· SMOTE Β· Pandas Β· NumPy

  • Built production-grade NIDS using ensemble ML models achieving high-accuracy threat detection
  • Reduced false positives by 25% and improved detection speed by 30% via SMOTE-based feature engineering

🀝 Connect with me

twitter linkedin medium leetcode

πŸ› οΈ Languages and Tools

javascript python cplusplus c html5 css3 react nextjs redux tailwind nodejs express
mongodb postgresql supabase prisma git postman figma scikit_learn pandas numpy linux framer

πŸ† Certifications & Achievements

  • πŸ₯‡ Top 7 β€” HackFest 2, GDG Cloud New Delhi | Multi-Agent AI pipeline among 500+ participants (Feb 2026)
  • πŸ“œ Stanford University Online β€” Divide and Conquer, Sorting and Searching, and Randomized Algorithms (Feb 2025)
  • ☁️ Google Cloud Skill Badge β€” Develop GenAI Apps with Gemini and Streamlit (Aug 2025)

πŸ“Š GitHub Stats

upasanaprabhakar

Β  upasanaprabhakar

upasanaprabhakar

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