B.E. Computer Engineering (AI/ML) with Honors in Data Science.
I am a student with a deep passion for building scalable, AI-driven solutions. My expertise lies in bridging the gap between machine learning research and production-ready applications.
- The Problem: Traditional video search relies on filenames/tags. This engine searches content using Natural Language.
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The Tech:
$SigLIP$ (Vision-Language Model),$FAISS$ Vector DB, Python, SQL. - Performance Metric: Optimized for speed—processed 25 minutes of video in just 21 seconds on a T4 GPU (Google Colab).
- Engineering Highlight: Built a cross-modal retrieval pipeline that maps text queries to video frame embeddings with high semantic accuracy.
- Impact: Fully operational in 2 retail outlets ("Cake of the Day").
- Value: Automated customer notifications and order management via WhatsApp API, significantly reducing manual coordination errors.
- Tech: Python, Automation Logic, API Integration.
- Languages: Python, C, SQL, Dart, Java.
- AI & ML: TensorFlow, SigLIP, FAISS, Scikit-Learn, CNNs, Computer Vision, NLP.
- Development: FastAPI, Git, CI/CD, Agile/Waterfall, Flutter, Docker, Linux.
- Audio Genre Classification: Developing a CNN-based architecture to classify music genres by processing audio files as Mel-spectrogram images.
- Technical Writing: Sharing my journey on Ready Tensor to help others build efficient AI agents.
- Certifications: Google (Data Analytics, IT Automation), IBM (ML & AI Internships), Edunet Foundation.
- Industry Prep: Completed job simulations for Electronic Arts (EA) and Accenture via Forage.