John Mackenzie
Vice President, Software Engineer at Goldman Sachs
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolJohn Mackenzie is a seasoned software engineering leader with 10 years of experience building and operating large-scale, auditable infrastructure and developer platforms from New York. As a Vice President and former Senior SRE, he has led cross-functional teams to design observability and CI/CD solutions, migrate monolithic tooling to GitLab, and standardize Python architectures across global projects. He excels at infra-as-code at scale—managing tens of thousands of Terraform lines across multi-cloud environments—and has built tooling (including REPL-driven Clojure wrappers and GraalVM CLIs) to make infrastructure safe and testable. His work spans configuration management, VM automation with NixOS, and secure application exposure via authenticated reverse proxies, always emphasizing reproducibility and compliance (SOC2 evidence tooling). Known for mentoring engineers and collaborating with ops and network teams, he translates operational constraints into maintainable software patterns. Counterintuitively for a VP, he still writes and ships low-level automation and developer-facing CLI tools that become team standards.
10 years of coding experience
5 years of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Purdue University
Github Skills (58)
deploying10
machine-learning-models10
jupyter10
data-science10
python10
machine-learning10
inference10
amazon-web-services10
reinforcement-learning10
sagemaker10
amazon-sagemaker10
vision-api10
huggingface10
mlops10
deep-learning10
Programming languages (5)
Github contributions (5)
easyj2j/sagemaker-python-sdk
Feb 2021 - May 2024
A library for training and deploying machine learning models on Amazon SageMaker
Contributions:10 pushes, 5 branches in 3 years 3 months
deployingsagemakeramazon-sagemakerdeploying-machine-learningamazon
easyj2j/amazon-sagemaker-examples
Jun 2020 - Feb 2021
Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker
Contributions:2 pushes in 8 months
sagemakeramazon-sagemakerdeep-learningreinforcement-learningamazon
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