Shanzia Shabnom Mithun
I build AI systems and document them clearly
New to Contra
Shanzia Shabnom is ready for their next project!
Automated Bilingual Document Intelligence System (RAG + LLM)
Built a retrieval-augmented generation system that allows users to query a specialized document corpus in both English and Bengali. The pipeline ingests source documents, indexes them with vector embeddings, and returns accurate, context-aware answers via an LLM. Designed for real-world use in water, sanitation, and hygiene (WASH) program contexts. Deployed live on Streamlit Cloud.
0
22
Automated Medical Image Analysis Pipeline with Domain Generalization
Developed a deep learning pipeline that classifies retinal fundus images across multiple disease categories, trained to generalize across unseen datasets using leave-one-domain-out evaluation. Built with PyTorch and deployed as an interactive Streamlit application. Part of active ML research exploring cross-dataset robustness for medical imaging. Combines research-grade methodology with production deployment.
0
31
Automated Semantic Search and Matching Engine (FAISS + SBERT)
Built a semantic product recommendation pipeline using FAISS for vector indexing and Sentence-BERT for generating dense embeddings. The system matches user queries to relevant products based on meaning not just keyword overlap, enabling highly relevant recommendations at scale. Fully deployed and live, demonstrating real-world vector database architecture on a production workload.
0
36
API-Triggered AI Dispatch Automation Pipeline
Built a live automated dispatch system that receives incoming service requests, analyzes them with an LLM, and routes field technicians intelligently, without human triage. The pipeline uses FastAPI as the backend engine, OpenAI for decision logic, and Streamlit for real-time monitoring. Designed and deployed solo from architecture through production. The system is live and publicly accessible.
0
40