Summary
Philippe Miron is a Principal AI Engineer with 11 years of experience blending research-grade fluid dynamics and nonlinear analysis with production AI and cloud-native engineering. He designs and deploys scalable agent-based systems and RAG pipelines on Kubernetes, currently building interconnected AI agents using Agent2Agent and Model Context Protocols to support enterprise customer service workflows. Previously he led high-performance precipitation estimation systems and ML-enabled ETL pipelines at DTN, delivering 10x performance gains, 99.9% reliability, and significant cost reductions. His academic work on Lagrangian coherent structures, vortex dynamics, and predictive models for oceanographic phenomena informs a pragmatic, physics-aware approach to ML and model design. Based in Miami, he teaches as adjunct faculty and brings a rare combination of field research, production ML, and frontend-to-infrastructure delivery experience.
11 years of coding experience
9 years of employment as a software developer
Ph. D. in Mechanical Engineering Experimental Fluid Dynamics, Ph. D. in Mechanical Engineering Experimental Fluid Dynamics at Polytechnique Montréal
BS Mechanical Engineering Ingeneria industrial (ETSII), BS Mechanical Engineering Ingeneria industrial (ETSII) at Universidad Politécnica de Madrid
French, English, Spanish, Portuguese