Alexander Calhoun

Alexander Calhoun leads the Agentic and AI Platforms Performance and Scale Engineering team at Red Hat, where he focuses on delivering enterprise-grade, production-ready AI systems on Red Hat OpenShift AI. In this role, Alexander drives end-to-end performance analysis across the full stack, evaluating and optimizing distributed AI architectures. His work centers on agentic workflows, large language models, and model inference engines such as vLLM to ensure high throughput, low latency, and predictability across cloud providers.

Previously, Alexander led performance and scale engineering for Red Hat’s In-Vehicle OS, establishing rigorous performance discipline across complex systems in safety-critical environments. His core expertise spans AI platform performance strategy, distributed systems benchmarking, cloud performance variability analysis, and data-driven engineering leadership.

Today, Alexander is focused on enabling Red Hat to deliver next-generation AI platforms with predictable performance, measurable scalability, and production credibility. He also actively integrates AI into performance analytics workflows to accelerate deep data analysis and elevate engineering decision velocity.