Hrishikesh332 (Hrishikesh Yadav) · GitHub
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Achievements

Achievement: Starstruckx2Achievement: Pull Sharkx2Achievement: QuickdrawAchievement: Pair Extraordinaire

Achievements

Achievement: Starstruckx2Achievement: Pull Sharkx2Achievement: QuickdrawAchievement: Pair Extraordinaire

Organizations

@tcet-opensource

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Hrishikesh332/README.md

Hrishikesh Yadav banner

Hi 👋, I'm Hrishikesh Yadav

Core AI Engineer | Machine Learning & Deep Learning | Multimodal AI & Video Understanding

  • 🔭 I’m currently building AI-powered products for video search, analysis, deep research, and multimodal retrieval

  • 🧠 My core experience spans Machine Learning, Deep Learning, Computer Vision, neural networks, model fine-tuning, optimization, and production inference

  • 🧩 My work extends beyond multimodal AI—from time-series modeling and deep learning frameworks for video to blockchain and DeFi applications powered by AI agents

  • ⏪ Previously, I worked as an AI Engineer at Shaga Labs

  • 📫 Reach me at hrishikesh3321@gmail.com

Recent Highlights:

  • 🏆 1st Place at the OpenClaw Hackathon by Growth × OpenAI × Hostinger — February 2026
  • 🌍 Global 5th Place at the BlueShift Agentic Hackathon

Connect with me:

Hrishikesh Yadav on LinkedIn Hrishikesh Yadav on X

Languages and Tools:

📋 Languages

Python C++ Java R TypeScript JavaScript

🎨 Frontend Stack

HTML5 CSS3 React Next.js Vite Streamlit

💾 Backend, Databases, and Vector Stores

Flask FastAPI PostgreSQL MongoDB Supabase Weaviate Qdrant Zilliz Milvus Chroma

🧪 Vector database experience - Hands-on experimentation and testing around Weaviate, Qdrant, Zilliz Milvus, Chroma, and other vector stores across retrieval workflows, indexing strategies, performance, RAG, and semantic search.

📙 AI and Data Libraries

PyTorch TensorFlow scikit-learn Pandas NumPy Lamini Unsloth

🛠️ Tools and Platforms

Git Docker Vercel GitHub

GitHub Streak:

Hrishikesh's GitHub contribution streak

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  1. ML_Guide ML_Guide Public

    One place to find variety of resources and a community of contributors, ML Guide is the perfect place to learn, build, and collaborate. Let's dive into the exciting world of machine learning together!

    Jupyter Notebook 89

  2. Twelve-Labs-Content-Recommendation Twelve-Labs-Content-Recommendation Public

    Content Recommendation is an open source platform that makes use of vector similarity search to provide highly relevant content recommendations.

    TypeScript 90 2

  3. Twelve-Labs-Security-Analysis Twelve-Labs-Security-Analysis Public

    Surveillance Analysis Tool powered by Twelve Labs is designed to automatically analyze video footage from surveillance videos.

    HTML 77 2

  4. Gemma-2-Tutorial-Guide Gemma-2-Tutorial-Guide Public

    Jupyter Notebook 77

  5. Weather-Forecasting-Using-ML Weather-Forecasting-Using-ML Public

    The aim of this project to see to do the prediction of the weather using the different types of machine learning model.

    Jupyter Notebook 80 6

  6. PowerBI_Projects PowerBI_Projects Public

    Python 78 2