🚀 ML Engineer | Backend Developer | Building Scalable AI Systems
💡 Focused on MLOps, Real-time ML, and Distributed Systems
📈 Passionate about turning data into production-ready intelligent systems

• Built end-to-end ML pipeline using LightGBM + SMOTE
• Improved Accuracy from 80.24 -> 82.22% accuracy.
• Designed scalable pipeline (data → training → evaluation->deployment)
• https://github.com/dvcodebase/YouTube-sentiment-analysis

• reducing manual reading effort by ~60–70%.
• end-to-end pipeline (PDF → preprocessing → model → summary)
• Improved training efficiency by tuning batch size, token limits, and GPU utilization, reducing training time and memory overhead.
• https://github.com/dvcodebase/TextSummarization

🚀 Improved model accuracy from ~80% → 82%+ ⚡ Reduced training time using optimized pipelines 📦 Built reproducible ML workflows with DVC 🔍 Processed large datasets (10K–100K+ samples)

supervised learning, classification, regression, model evaluation

Docker, MLFlow, DVC, AWS

• System Design (Scalability, Load Balancing)
• Distributed Systems

• Built multiple end-to-end ML projects
• Hands-on experience with real-world datasets
• Actively improving DSA & problem-solving skills

I love building systems that are not just intelligent, but also scalable and production-ready 🚀