Klaas Fiete Krutein
Senior Applied Scientist @Amazon
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WORK HISTORY
Senior Applied Scientist @Amazon
Seattle, WA, US
Lead the design and evolution of large-scale decision systems combining optimization, simulation, and Generative AI, setting technical direction across projects and domains • Drive end-to-end ownership of science initiatives, aligning stakeholders across science, engineering, and product to deliver impactful, production-ready solutions • Act as a technical leader and thought partner, shaping roadmaps and influencing how decision systems are designed, built, and adopted across the organization • Build and scale team capabilities by mentoring scientists, guiding system design, and establishing best practices for combining operations research with modern AI approaches • Designed and introduced context-aware, memory-enabled multi-agent AI systems that enhance how teams interact with models, code, and decision workflows • Champion the integration of Generative AI into scientific workflows, enabling more efficient development, improved knowledge sharing, and increased team productivity • Drive adoption and trust in decision systems by translating complex modeling approaches into clear, actionable insights for diverse stakeholders • Contribute to a culture of technical excellence, ownership, and continuous improvement, positioning the team for long-term growth and impact
EDUCATION
University of Washington
Master of Science (MS), Industrial Engineering
University of Washington
Doctor of Philosophy (PhD), Industrial Engineering
University of Auckland
Visiting Student (no degree), Industrial Engineering and Management
Ernst-Barlach-Gymnasium Kiel
High School, Mathematics, English, German, Music
Fachhochschule Nordakademie Elmshorn
Bachelor of Science (BS), Industrial Engineering & Business Management (Wirtschaftsingenieurwesen)
SKILLS
ABOUT KLAAS FIETE KRUTEIN
I am a Senior Applied Scientist working on large-scale decision systems at the intersection of optimization, simulation, and Generative AI.My core expertise is in operations research, particularly stochastic optimization and simulation, applied to complex planning problems under uncertainty. I focus on building end-to-end decision systems that translate mathematical models into production environments and real-world impact.More recently, I have been extending these systems with Generative AI to improve how science is developed and used in practice. This includes building context-aware, memory-enabled multi-agent AI systems that act as “science assistants,” helping teams interact more effectively with models, code, and decision logic.My work sits at the intersection of:• Optimization & simulation for decision-making under uncertainty • Scalable, production-grade decision systems • Generative AI and agent-based systems for scientific workflows • Bridging research, engineering, and product to drive adoption I am particularly interested in how classical optimization and modern AI can be combined to create more adaptive, interpretable, and scalable decision systems.Always happy to connect with others working on decision intelligence, operations research, and applied AI.
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