AI Engineer • Computer Vision • Data Science • RAG Systems
I build practical AI systems that combine machine learning, deep learning, computer vision, data analytics, and retrieval-augmented generation.
Deep-learning surveillance analytics system that classifies CCTV footage as Normal, Violence, or Weaponized, detects when incidents occur, and generates explainable review reports.
Tech: Python • PyTorch • ResNet18 • LSTM • OpenCV • YOLO • Streamlit
Retrieval-Augmented Generation system for football analytics using structured data, hybrid retrieval, BM25, dense embeddings, vector databases, and LLM-powered answers.
Tech: Python • RAG • BM25 • Embeddings • ChromaDB • Streamlit • LLMs
End-to-end retail data analysis covering customer segmentation, market basket analysis, sales patterns, returns, and business recommendations.
Tech: Python • Pandas • NumPy • Data Visualization • EDA • Customer Segmentation
AI & Machine Learning:
Deep Learning • Computer Vision • NLP • RAG • Embeddings • LLM Integration
Data:
Pandas • NumPy • Data Analysis • Visualization • Feature Engineering
Tools & Deployment:
Git • GitHub • Streamlit • Docker • REST APIs
- Building production-ready AI & Computer Vision systems
- Developing RAG applications with reliable retrieval and evaluation
- Turning AI models into complete usable applications
- Improving model explainability and real-world deployment
Artificial Intelligence Computer Vision Deep Learning RAG Data Science Python
Build • Evaluate • Improve • Deploy