I love building projects that solve real-world problems, compete in programming contests, and explore the frontiers of AI. Currently, I’m diving deep into, Gen-AI, NLP ,Deep-Learning, and scalable ML systems, Computer Vision.
- Programming Languages: Python, C++, C
- Data Science & ML: Pandas, NumPy, scikit-learn, RAG, Tensorflow, Keras, NLTK, Embedded Systems, Recommendation System
- Web Development: Flask, HTML, CSS, JS, Jinja2
- Computer Vision: OpenCV, MediaPipe
- Databases: Vector-Databases for RAG
- Tools & Others: Git, VS Code, Jupyter Notebook, Linux
- The Build: Developed a RAG pipeline using CyborgDB and LangChain to encrypt vector embeddings, ensuring data privacy in forensic retrieval.
- Key Innovation: Engineered an intelligent router to toggle between low-latency chat and deep retrieval; benchmarked at 13 QPS with a minimal 4.2% privacy overhead.
- Tech: Python, CyborgDB, LangChain, Flask, Vector Encryption.
- The Build: Architected a pipeline to extract structured data from multilingual (Hinglish) medical journals using Sentence-Transformers.
- Key Innovation: Eliminated hallucinations by engineering Pydantic guardrails and a Semantic N-gram Scorer (mBERT) to ensure cross-lingual term grounding.
- Tech: Python, Sentence-Transformer, Pydantic, LangChain, Multilingual-BERT.
- The Build: Fine-tuned ResNet50 and implemented MediaPipe for face alignment, standardizing inputs against pose and lighting variations.
- Key Innovation: Boosted accuracy from 45% to 83.4% through layer unfreezing and data augmentation; utilized L2-Normalized Embeddings for precise one-shot matching.
- Tech: Python, TensorFlow, MediaPipe, Scikit-Learn, Flask.
- Advance RAG
- Agentic AI
LinkedIn(#)