Jeannie Finks
Greater Boston United States
Summary
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RockstarJeannie Finks is a results-driven digital solutions leader with 25+ years of experience in customer success, digital strategy, implementation, and operations, based in Greater Boston. She excels at translating complex technical concepts for diverse audiences and simultaneously managing customer, partner, and internal relationships to maintain exceptional client satisfaction. Known for inventive problem-solving, she blends people, process, and product knowledge to design resourceful solutions that boost service levels and operational efficiency. In addition to strategic and delivery work, Jeannie contributes to open-source MLOps efforts—improving documentation, community standards, and CPU build processes for the sparsity-aware DeepSparse inference runtime—highlighting her hands-on focus on usability and performance. Colleagues trust her for rapid relationship-building and cross-functional influence, making her a dependable leader during complex transformations. Her profile reflects a rare mix of strategic vision and practical engineering-minded contributions that drive measurable business outcomes.
5 years of coding experience
Github Skills (7)
documentation10
python9
inference8
machine-learning8
onnx8
github-ci6
githubaction-workflow6
Programming languages (3)
Github contributions (5)
neuralmagic/deepsparse
Jan 2021 - Nov 2022
Role in this project:
MLOps Engineer
MLOps EngineerContributions:37 releases, 296 reviews, 40 commits in 1 year 9 months
Contributions summary:Jeannie primarily contributed to the project's documentation, making significant updates and improvements to the `docs/source/index.rst` file, adding links, and correcting URLs. They also updated the `CODE_OF_CONDUCT.md` and `CONTRIBUTING.md` files, enhancing the project's community guidelines. Furthermore, the user implemented modifications to the build process, introducing new features to the `cpu.py` file. These contributions suggest a focus on improving the usability and maintainability of the project and its documentation, as well as optimizing hardware capabilities.
llm-inferenceruntimetensorflowsparsificationmachinelearning
neuralmagic/sparsify
Jan 2021 - Nov 2022
ML model optimization product to accelerate inference.
Contributions:21 releases, 33 reviews, 35 commits in 1 year 10 months
image-classificationtensorflowsparsification-recipessmaller-modelssparsification
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