Jeremy Mcminis is a Principal Engineer and seasoned AI/data engineering leader with 19 years of experience building production-grade systems across startups and enterprise environments. Based in Ann Arbor, he combines hands-on engineering—shipping LLM-powered applications, RAG systems, and scalable data platforms—with strategic technical leadership as a founder, fractional CTO, and principal R&D engineer. He has a track record of designing data warehouses, ML platforms, and human-in-the-loop workflows that move AI from prototype to revenue-generating product while minimizing technical debt. An active open-source contributor, he’s implemented core features for high-performance scientific software (QMCPACK) and improved developer-facing integrations in widely used tooling like Rasa. Comfortable from low-level algorithm tweaks to board-level architecture, he blends a physicist’s rigor (PhD, UIUC) with a pragmatic taste for simple, obviously correct solutions. Outside work he’s equally drawn to coffee, data, and startups—often turning experimental ideas into practical products.
19 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Physics, Doctor of Philosophy (Ph.D.) Physics at University of Illinois Urbana-Champaign
Bachelor of Science - BS Physics, Bachelor of Science - BS Physics at Eastern Michigan University
Main repository for QMCPACK, an open-source production level many-body ab initio Quantum Monte Carlo code for computing the electronic structure of atoms, molecules, and solids with full performance portable GPU support
Role in this project:
Back-end Developer
Contributions:647 commits, 1 comment, 1 issue in 6 years 3 months
Contributions summary:Jeremy implemented new features and made bug fixes related to the core functionality of the QMCPACK code. This included removing and adding trial energy components and making modifications to improve existing algorithms, primarily working with the walker configuration. The user's contributions are evident in the modifications made to code files, such as src/Particle/Walker.h and src/QMCHamiltonians/LocalMomentEstimator.cpp, as well as in the creation and modifications of various QMC drivers.
💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants
Role in this project:
Full-stack Developer
Contributions:11 commits, 1 PR, 9 comments in 1 month
Contributions summary:Jeremy primarily focused on enhancing the Rasa framework, specifically within the Jupyter Notebook integration. Their contributions included adding an `endpoints` parameter to the `rasa.jupyter.chat` method, allowing users to utilize custom action servers. They also made improvements to the documentation, including adding a changelog entry and fixing an issue with the lexer class. Furthermore, the user made updates related to the `FeatureSignature` and adjustments to the entity extraction process.
machine-learningnluslackvoice-assistantnlp
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