Monir Moniruzzaman is a Senior Applied Scientist in the Greater Seattle area with eight years of hands-on experience building AI-driven solutions for large-scale products at Oracle and Amazon. He designs and ships practical ML systems—from synthetic data generation agents with automated prompt optimization to fine-tuned GPT models that improve user intent understanding and tool use. Previously he developed scalable hyperparameter optimization and AutoML tools at HPE/Cray and co-authored a multimodal transformer paper used to predict conversational assistant quality at Amazon. His background spans applied research (PhD-level work in data privacy and published papers) to production engineering, enabling rapid iteration between research hypotheses and deployable models. He’s adept at reducing operational friction—evidenced by an LLM evaluation framework adopted by 100+ engineers—and brings uncommon domain breadth, including embedded automotive software and patented GCN work for oilfield prediction.
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