Shaad Fazal is a Machine Learning Resident with the Advanced Technology team at Amii. He is currently focused on developing AI-assisted tools for geotechnical site characterization, building efficient systems that leverage OCR and Large Language Models to extract and standardize engineering parameters from scientific reports to generate meaningful ML-driven insights. Shaad has experience in designing and deploying agentic AI workflows and RAG-based systems, including the creation of conversational agents designed to bridge the gap between complex data and human interaction.

Previously, as a Generative AI Associate at Innodata, Shaad focused on the testing, evaluation, and optimization of agentic Chain-of-Thought (CoT) prompting for Vision Language Models (VLMs), with an emphasis on enhancing spatial reasoning and multimodal alignment. Shaad earned his Master of Applied Science in Electrical and Computer Engineering from Carleton University, where his research and thesis track centered on signal and image processing. During his graduate studies, he developed Generative AI and computer vision-based solutions for medical imaging applications, specializing in high-performance image segmentation and predictive modeling.

Outside of work, Shaad is a console gaming enthusiast and stays active through table tennis and football.