Shah Noor

I am an entry-level AI/ML systems engineer and recent Computer Science graduate focused on building production-ready, real-time machine learning systems. I combine strong fundamentals in ML, deep learning, NLP and transformers with practical experience in MLOps, DataOps and cloud technologies.

For my final year project I engineered a production-grade, scalable ML pipeline to forecast crypto prices using Kafka, Docker, Kubernetes and Hopsworks, and reduced model latency by 40% through asynchronous REST API optimization with Flask. I automated training and deployment with CI/CD in GitHub Actions and managed model versioning with CometML.

I have built both streaming and batch systems — including a real-time crypto price predictor and an NYC taxi demand batch predictor with a Streamlit dashboard — emphasizing modular design, fault-tolerant ingestion, and resilient data flow using Redpanda and Quix Cloud.

I am motivated to join tech-driven teams where I can apply MLOps, cloud platforms (AWS/GCP/Azure), and automation to deploy scalable AI solutions, while continuing to learn modern LLM architectures and system-level best practices.