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Resham011/README.md

Resham

AI/ML Engineer Β Β·Β  GenAI Developer Β Β·Β  Software Engineer

I build production-grade AI systems β€” RAG pipelines, LLM applications, and intelligent full-stack products that solve real problems.


πŸ‘€ About Me

  • πŸŽ“ B.Tech CSE @ Quantum University β€” CGPA 8.3 | Graduating 2026
  • 🧠 Specialized in Generative AI Β· RAG Β· NLP Β· Computer Vision Β· LLM Applications
  • πŸ’» 821+ LeetCode problems | Global Rank 46,622 (Top 6%) | Rating 1561
  • πŸ† Top 7 Finalist β€” HackIndia Hackathon Β |Β  2nd Place β€” Genesis Idea Open House 2023
  • πŸš€ Actively seeking AI/ML Engineer / GenAI Developer roles (Full-time & Internship)

πŸ› οΈ Tech Stack

Programming

Python C++ Java SQL

ML / DL Frameworks

PyTorch Scikit-learn HuggingFace Computer Vision

GenAI / LLM

LangChain RAG LLMs FAISS Chroma DB Groq

Data Tools

Pandas NumPy Jupyter

Dev & Web

FastAPI React Next.js Node.js MongoDB Docker Git TailwindCSS


πŸš€ Featured Projects

Problem: Extracting accurate, cited answers from multiple PDFs at scale with low latency.

FastAPI LangChain FAISS Llama-3-70B (Groq) Cohere Embeddings Next.js

  • πŸ“ˆ Retrieval accuracy: 68% β†’ 84% (validated on 150 real-world queries)
  • ⚑ Latency: 3.1s β†’ 1.5s via Groq inference + async FastAPI + embedding caching
  • πŸ›‘οΈ Citation grounding + fallback responses to mitigate hallucinations
  • πŸ”§ Pipeline: PDF ingestion β†’ 800/150-token chunking β†’ embeddings β†’ FAISS β†’ MMR retrieval β†’ LLM synthesis

πŸ”— Live Β |Β  πŸ“‚ GitHub Β |Β  🎬 Demo


Problem: Single-model recommenders fail to capture visual + behavioral user preferences together.

PyTorch MobileNetV2 FastAPI Scikit-learn

  • 🎯 88% validation accuracy combining collaborative filtering, metadata similarity, and visual feature extraction
  • πŸ“· Applied Computer Vision (MobileNetV2) for image-based item matching
  • πŸ—οΈ Modular, testable backend API architecture β€” scalable to production

Problem: Inconsistent revision and poor long-term retention of DSA concepts during interview prep.

React.js Node.js MongoDB JWT

  • πŸ“Š Revision consistency improved by 40% via spaced-repetition + decay scheduling
  • πŸ—‚οΈ 500+ problems logged Β· 20+ active users Β· personalized modes (by company, topic, random)
  • πŸ” JWT auth with refresh token rotation Β· daily streak tracking Β· analytics dashboard

πŸ”— Live Β |Β  πŸ“‚ GitHub Β |Β  🎬 Demo


Problem: Discovering similar movies based on plot, genre, and cast β€” fast and accurately.

Python Scikit-learn TF-IDF NLP Streamlit TMDB API

  • ⚑ 60% faster lookups via precomputed similarity matrix + NumPy vectorization
  • βœ… Validated across 50+ real-world searches
  • 🎨 Interactive Streamlit app with live TMDB poster previews

πŸ”— Live Β |Β  πŸ“‚ GitHub


πŸ’Ό Experience

🌐 Web Content Intern β€” GAO Tek Inc. Aug 2024 – Oct 2024 Β· Remote

  • Optimized 100+ product listings for SEO using metadata and keyword analytics

πŸ† Achievements

πŸ’» 821+ LeetCode problems Β· Global Rank 46,622 (Top 6%) Β· Rating 1561
πŸš€ Top 7 Finalist β€” HackIndia Hackathon
πŸ₯ˆ 2nd Place β€” Genesis Idea Open House 2023 (Startup)
πŸŽ–οΈ Participant β€” Adobe India Hackathon
πŸ“œ HP LIFE AI Fundamentals Β· Udemy SQL Bootcamp

πŸ“Š GitHub Stats


Open to AI/ML, GenAI, and Software Engineering roles Β· Full-time & Internship Β· 2026

Pinned Loading

  1. Movie-Recommendation-System Movie-Recommendation-System Public

    🎬 A Content-Based Movie Recommender System using Python, Scikit-Learn, and Streamlit. Features 5,000+ movies with real-time poster fetching via TMDB API. πŸš€

    Jupyter Notebook

  2. pdf-rag-chatbot pdf-rag-chatbot Public

    AskMyPDF AI is a smart digital assistant that lets you "talk" to your PDF documents to get instant answers without reading through every page.

    TypeScript 1

  3. Abaya-and-Thobes-Recommendation-System Abaya-and-Thobes-Recommendation-System Public

    Jupyter Notebook

  4. DSA-Master-Sheet DSA-Master-Sheet Public

    C++

  5. AlgoRecall AlgoRecall Public

    AlgoRecall is a smart web app I built to help developers retain DSA knowledge for coding interviews. Using a spaced repetition system, it creates daily custom-tailored revision sessions that adapt …

    JavaScript 2