Ytsen De Boer is an AI engineer and systems programmer with 11 years of experience, blending a PhD-level math/physics background with production-grade data science and low-level systems expertise. Currently on an AI research sabbatical, he focuses on full-stack LLM engineering—from NVIDIA Blackwell inference optimization and FP8/FP4 KV-cache tuning to advanced RAG architectures and automated LLM self-auditing. He has driven end-to-end deployments and cost/observability tooling on Azure OpenAI, built sovereign 32B summarisation pipelines, and compiled high-performance inference engines for RTX 5090-class hardware. Previously he scaled ML-driven automation in fintech, insurance claims, and cyber-security, while leading teams and formalising engineering standards and CI/CD practices. Known for a calm, attentive leadership style, he pairs pragmatic simplicity with deep curiosity about low-level GPU stacks and hardware-constrained fine-tuning workflows. Based in Sneek, Netherlands, he brings a rare mix of research rigor and hands-on systems optimization that repeatedly turns complex problems into reliable, auditable systems.
11 years of coding experience
14 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Experimental Particle Physics, Doctor of Philosophy (Ph.D.) Experimental Particle Physics at University of Twente
Contributions:45 commits, 41 pushes, 4 branches in 20 days
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