Summary
Cameron Quilici is a machine learning and AI engineer with six years of software experience and dual BS degrees in Computer Science and Applied Mathematics from Texas A&M. He has built production AI infrastructure—designing scalable model deployment APIs, CI/CD pipelines, and dynamic model caching for Kubernetes environments—and driven MLCommons inference benchmarking and inference workflows. His background blends research (an undergraduate thesis on GraphBLAS graph clustering) with hands-on systems work in cloud and HPC tooling, Terraform/Ansible automation, and backend API design. At Hewlett Packard Enterprise and SemiAnalysis he translated customer signals into production ML solutions using transcription, LLM summarization, and topic analysis. Cameron’s strength is working at the intersection of rigorous mathematics and practical engineering to shrink model deployment time and improve performance, with a particular interest in graph algorithms and scientific computing.
6 years of coding experience
2 years of employment as a software developer
Bachelor of Science - BS, Computer Science, Applied Mathematics, 3.956 (Alumni), Bachelor of Science - BS, Computer Science, Applied Mathematics, 3.956 (Alumni) at Texas A&M University
The Woodlands High School