Shaheer Jamal

I am an AI/ML engineer focused on building production-ready machine learning systems, from data ingestion to deployment. I specialize in MLOps, computer vision, and NLP, and I prioritize reproducible pipelines and automated retraining.

At 10Pearls, I architected a serverless end-to-end ML pipeline for real-time AQI forecasting with automated data ingestion and preprocessing for time-series environmental data. I built multi-model training frameworks integrated with a Hopsworks feature store for experiment tracking and version control.

In prior internships I developed a RAG-based legal document querying system using LangChain and ChromaDB, engineered document processing for 300+ page documents, and achieved 85%+ accuracy through retrieval optimization and prompt engineering. I also developed chronic kidney disease prediction models and interactive BI dashboards using IBM watsonx.ai and IBM Cognos Analytics.

My projects include an XGBoost-based heart disease prediction MLOps pipeline with continuous retraining, Hopsworks feature store integration, and a deployed Streamlit app, plus NutriScan — a PyTorch computer vision system deployed on Azure ML with REST APIs and a MongoDB backend. I seek roles where I can deliver end-to-end ML products and improve model reliability in production.