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I am the founder of Paralleliq, a cloud-native GPU infrastructure optimization platform that helps AI teams recover wasted compute capacity without changing their stack. Before starting Paralleliq, I built deep expertise across distributed systems, AI infrastructure, and ML platform engineering. Paralleliq's optimization engine is rules-based and deterministic — designed around the principle that infrastructure decisions require human approval, full auditability, and zero reliance on autonomous agents. I work directly with GPU cloud providers, enterprise AI teams, and inference platforms to close the gap between what monitoring sees and what clusters are actually doing.