Machine Learning Engineer | Medical Imaging, Computer Vision & AI Applications
I build deep learning systems and AI-powered applications that solve real problems, from diabetic retinopathy detection to automated career gap analysis.
- Medical Image Analysis — retinal scans, MRI, CT imaging
- Deep Learning — EfficientNet, ResNet, U-Net architectures
- Interpretable AI — Grad-CAM visualizations for clinical explainability
- AI-Powered Applications — full-stack apps powered by large language models
- End-to-End Systems — model training to deployed web applications
| Project | Description | Stack |
|---|---|---|
| RetinaLens AI | Diabetic retinopathy detection across 5 severity grades with Grad-CAM | PyTorch, EfficientNet-B4, Flask |
| SkillBridge AI | AI career gap analyser — upload your CV, get a personalised skill gap analysis, learning roadmap and CV tips for any role | Python, Flask, Groq, Llama 3.3 70B |
| Brain Tumour Segmentation | Automated MRI tumour segmentation using U-Net | PyTorch, U-Net |
| Lung Cancer Detection | CT scan classification using ResNet50 | PyTorch, ResNet50 |
