Eric Curtin is a Principal Software Engineer with 11 years of experience building robust, low-level systems and AI tooling from kernel and storage stacks to LLM inference. Based in Ireland, he has driven critical features for automotive and embedded platforms at Red Hat—leading OTA, ostree, A/B upgrades and Android bootloader integration—before moving to AI-focused work at Docker on llama.cpp and Docker Model Runner. He combines deep C/C++ expertise (kernel, protocols, memory safety) with DevOps discipline, having improved build, static analysis, sanitizers and CI workflows for popular open-source projects like inotify-tools and llama.cpp. Eric’s contributions include practical memory-safety refactors (smart pointers), tooling for model inference, and automated multi-compiler static builds, reflecting a focus on production reliability and reproducibility. He holds an M.Sc. in Software Engineering & Database Technologies and a record of patent-backed innovation in platform integration. Colleagues describe him as a pragmatic engineer who bridges systems-level rigor with practical AI deployment.
11 years of coding experience
13 years of employment as a software developer
Master of Science (M.Sc.) Software Engineering & Database Technologies, Master of Science (M.Sc.) Software Engineering & Database Technologies at University of Galway
Bachelor of Science (B.Sc.) Computer Systems, Bachelor of Science (B.Sc.) Computer Systems at University of Limerick
inotify-tools is a C library and a set of command-line programs providing a simple interface to inotify.
Role in this project:
Back-end Developer & DevOps Engineer
Contributions:15 releases, 25 reviews, 135 commits in 2 years 11 months
Contributions summary:Eric primarily contributed to the improvement of the `inotify-tools` project, focusing on enhancing build processes and code quality. They fixed compiler warnings and potential buffer overflows in the C code. The user also added build status to the README and automated builds with both GCC and Clang, including static builds. Furthermore, they implemented code coverage and address sanitizer builds and integrated clang-tidy and cppcheck to improve the code quality.
Contributions:93 reviews, 66 PRs, 85 pushes in 9 months
Contributions summary:Eric implemented the `llama-run` example program, creating a new tool for LLM inference in C/C++. They refactored the code using smart pointers to manage memory, reducing the risk of memory leaks, and split the code into smaller functions. The user added positional argument handling for the model and prompt, along with support for various model download protocols. Further improvements included adding a temperature option and fixing context size settings.
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Eric Curtin - Principal Software Engineer at Docker, Inc