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Achievement: YOLOAchievement: Pull Shark

Achievements

Achievement: YOLOAchievement: Pull Shark

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  • Pro

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@Quantrel-Labs

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SahilChambyal/README.md

🚀 Top 0.5% in Amazon ML Challenge 2025 • 🎯 Microsoft Founder's Hub Member • 🔬 AI/ML @ Quantrel


Profile Views   Portfolio

🎓 B.Tech CSE @ Lovely Professional University (CGPA: 8.33)
💼 AI/ML @ Quantrel - Building next-gen LLM pipelines
🎖️ NCC Cadet - 6 years of service with A & B Certificates
📍 India | Open to Global Opportunities

💫 About Me

const sahil = {
    pronouns: "He/Him",
    role: "AI/ML Engineer",
    location: "India 🇮🇳",
    currentlyBuilding: "Agentic AI Systems & LLM Applications",
    education: {
        degree: "B.Tech in Computer Science",
        specialization: "Machine Learning & Deep Learning",
        university: "Lovely Professional University"
    },
    achievements: {
        amazonMLChallenge: "Top 0.5% (79th/20,000+ teams)",
        microsoftFoundersHub: "$20,000 in benefits",
        patentFiled: "Hybrid ViT Model for Crop Diagnosis"
    },
    motto: "Building AI that makes a difference 🚀"
};

🛠️ Tech Stack

🤖 AI/ML & Deep Learning

🔤 Languages

🧰 Frameworks & Tools

☁️ Cloud & Databases

⚙️ DevOps & Others

💼 Experience

🧠 AI/ML @ Quantrel

  • ⚡ Built high-throughput text-tokenization pipelines achieving 30-40% faster preprocessing
  • 🔍 Developed web-search tool for agentic models - 25% improvement in answer accuracy
  • 📊 Designed credit-consumption prediction algorithm with 90-95% cost-prediction accuracy

🚀 Featured Projects

🌾 Hybrid ViT for Crop Diagnosis

Patent Filed

  • Architecture: ViT + CNN + Custom MLP
  • Accuracy: 99.5% validation accuracy
  • Classes: 38 crop disease classifications
  • Feature: IoT-ready inference pipeline
  • Impact: Treatment recommendations included

🤖 ORIN Tri-Sense AI

Production

  • Type: Multilingual RAG Chat Runtime
  • Languages: 6+ supported
  • Performance: 200 QPS, <300ms latency
  • Relevance: 86% with 0.72 MRR
  • Impact: 96hrs → 10min response time

💰 DeBERTa Price Regressor

Kaggle

  • Model: DeBERTa-v3-base (183M params)
  • Dataset: 75,000 samples
  • SMAPE: 21.73% (from 63.17%)
  • Training: 30 epochs with AMP
  • GPU: Tesla P100-PCIE-16GB

🏆 Amazon ML Challenge 2025

Top 0.5%

  • Ranking: Top 0.5% globally
  • Position: 79th out of 20,000+ teams
  • Score: SMAPE 42.9%
  • Best Score: 39.2% (top participant)

📜 Certifications





🏆 Achievements & Recognition


79th / 20,000+ teams
Founder's Hub Member
6 Years of Service
Hybrid ViT Model

🤝 Let's Connect!


💭 "The best way to predict the future is to create it." — Peter Drucker

github contribution grid snake animation
⭐ From SahilChambyal with 💜

Pinned Loading

  1. Neural_Network_from_scratch_MNIST Neural_Network_from_scratch_MNIST Public

    Jupyter Notebook

  2. NLP_text_analysis_web-scrapping NLP_text_analysis_web-scrapping Public

    Jupyter Notebook

  3. GadgetRecommendationSystem-using-ML GadgetRecommendationSystem-using-ML Public

    A Machine Learning Project to help out with Gadget Recommendations

    Jupyter Notebook

  4. food-order-next-firebase-ts food-order-next-firebase-ts Public

    TypeScript 1

  5. portfolio portfolio Public

    running live

    TypeScript 1