Senior Computer Vision & Deep Learning Engineer with an MSc in IoT & Data Science. Specializing in high-accuracy YOLO pipelines, edge deployment, and production-ready AI systems.
Dedicated to building AI systems that solve real-world challenges. From manufacturing defect detection to medical imaging, I deliver production-ready solutions that combine state-of-the-art models with robust engineering.
Fine-tuning SOTA models (YOLOv8, YOLOv9, CLIP) for niche industrial data.
Deploying robust apps with Streamlit, Flask, and cloud-native Docker containers.
Leading car damage detection pipelines and high-precision annotation workflows for automotive systems.
Architecting analytics pipelines for large-scale security datasets and automated KPI generation.
Delivered high-impact vision models for infrastructure monitoring and asset management.
Developed real-time pose and gait estimation models for healthcare monitoring using MediaPipe and open-source frameworks.
Contributed to large-scale video analytics and IoT-based urban safety initiatives.
A showcase of my technical capabilities.
Industrial Streamlit application identifying sub-mm defects in production lines using custom YOLOv8 backbone.
Deep learning pipeline for MRI analysis, utilizing YOLOv9 for high-recall tumor localization.
Surveillance-grade AI system for public safety monitoring with zero-lag inference capabilities.
Improved YOLO architecture specifically optimized for small object detection in dense manufacturing environments.
Validated real-time patient monitoring system using MediaPipe for mobility analysis in clinical settings.
National-scale data visualization platform for crime distribution and police workload distribution.
Exploring the architectural shifts in YOLOv9 and how it impacts inference speed.
Aug 01, 2025A comprehensive guide to containerizing vision pipelines for scalable production.
Jul 20, 2025How to combat small datasets using advanced geometric and pixel-level augmentations.