Taysseer Sharaf

Spearheaded Guardian’s GenAI and data science strategy, delivering scalable AI solutions across manufacturing and commercial sectors.

• Oversaw full AI product development lifecycle, from platform architecture to deployment and continuous enhancement.
• Engineered scalable GenAI infrastructure, featuring a RAG-ready vectorization pipeline and reusable multi-agent orchestration framework.
• Directed delivery of enterprise AI applications that improved decision-making and automated onboarding processes.
• Mentored engineers and interns to develop high-performing GenAI capabilities, fostering a robust AI culture.
• Championed LLM-Ops practices utilizing Azure AI Foundry and Snowflake for reliable enterprise deployment.
• Enhanced technical knowledge access across functions through innovative AI application development.
• Reduced development time and accelerated AI product delivery through optimized knowledge management systems.

• Designed and implemented cost-efficient data science delivery framework to streamline use case identification and production.
• Delivered high-impact models, including logistics optimization product, generating $2M in annual savings.
• Coordinated cross-functional teams of data scientists, engineers, and developers to ensure timely project delivery.

• Developed time-series forecasting models to predict monthly customer orders by product segment, enabling the supply chain team to improve demand planning accuracy and optimize production scheduling.
• Co-developed a predictive inventory management model for the procurement team, enhancing visibility into material requirements, reducing stockouts, and improving working capital efficiency.
• Designed and deployed a self-service analytics dashboard that allowed plant engineers to build ML models with integrated explainability features—helping them identify key process drivers of defects and implement targeted improvements, resulting in $1.7M in cost savings.

• Conducted internationally recognized research, applying advanced logistic regression modeling on an international social science project in Nepal to identify key factors causing farmer exits from agriculture.
• Led a federally funded project analyzing survival of young leukemia patients in the U.S., managing professionals and utilizing advanced data mining techniques on National Cancer Institute datasets to uncover critical survival factors.
• Developed Bayesian neural network package in R, innovating statistical methodologies and enhancing accuracy of neural network models.
• Supervised undergraduate research projects, guiding student teams in predictive analytics and data mining on large datasets, yielding actionable insights and enriching learning outcomes.