Carl Elgin is a Machine Learning Engineer with a decade of experience designing and deploying production-grade ML systems that bridge data, cloud infrastructure, and edge instrumentation. Based in Boulder, he has led end-to-end development of AI-driven data quality tooling for globally deployed industrial sensors and built cloud-native ML pipelines and diagnostic services using Python, SQL, and Google Cloud. As an independent contractor and consultant he applies reinforcement learning, NLP/LLMs, and bespoke modeling to real-world reliability and diagnostics problems, turning noisy sensor streams into actionable corrections and recommendations. His background in enterprise storage and capacity analytics gives him a systems-minded approach to scalability and observability that few ML engineers emphasize. Carl holds an MS in Statistics from KU Leuven and combines research-driven model development with pragmatic production practices like CI, version control, and automated deployments.
9 years of coding experience
4 years of employment as a software developer
Master of Science - MS Statistics, Master of Science - MS Statistics at KU Leuven
Bachelor of Science in Informatics Concentration in Social Computing, Bachelor of Science in Informatics Concentration in Social Computing at University of Michigan
Contributions:4 PRs, 22 pushes, 5 branches in 1 day
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