I am an ML Engineer specializing in the end-to-end development and deployment of computer vision systems. I have a proven ability to architect robust AI/ML pipelines, containerize models for scalable inference via APIs, and integrate ethical considerations and explainability into the core of a project. I am passionate about building responsible and transparent AI solutions.
| Project | Description | Technologies Used |
|---|---|---|
| Deepfake Detection | Engineered an end-to-end deepfake detection pipeline achieving 91.2% accuracy. Deployed as a scalable REST API using FastAPI and Docker with Grad-CAM for explainability. | PyTorch, FastAPI, Docker, OpenCV, XAI |
| Video Anomaly Detection | Built a production-grade anomaly detection system using a PyTorch-based autoencoder (92.5% precision). Architected with a full MLOps pipeline for real-time stream analysis. | PyTorch, FastAPI, Docker, MLOps, Render |
| AI Visual Search Engine | Architected a visual search system for 100K+ images with <100ms latency using CLIP and FAISS. Deployed with a full-stack ML pipeline featuring async endpoints and a Streamlit UI. | CLIP, FAISS, PyTorch, FastAPI, Streamlit |
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