Ray Douglass
Consulting at Freelance
United States
Summary
🤩
Rockstar🎓
Top SchoolRay Douglass is a seasoned DevOps and software engineering leader with 11 years of experience building and automating CI/CD, package distribution, and deployment infrastructure—most recently managing RAPIDS operations at NVIDIA. He blends hands-on backend work (CUDA-accelerated geospatial and ML libraries like cuspatial and cuDF) with deep build-and-release expertise, having authored build scripts, manylinux support, and automated conda/package releases. Ray has a track record of performance-driven systems work—from optimizing Java web services and custom cache stores to accelerating geospatial processing with CUDA—and a pragmatic focus on “automate everything.” Comfortable leading teams and shipping developer-facing infrastructure, he pairs technical breadth (DevOps, CUDA, Docker, conda, CI) with business acumen from an MBA. Now consulting, he brings rare cross-cutting experience that spans low-level GPU-accelerated libraries to production release engineering.
11 years of coding experience
14 years of employment as a software developer
Master of Business Administration (MBA), Master of Business Administration (MBA) at University of Maryland Global CampusBachelor of Science (BS), Computer Science, Bachelor of Science (BS), Computer Science at University of Maryland
Github Skills (28)
github-ci10
docker10
c-language10
python10
bash10
conda10
cmake10
geospatial10
dockers10
cicd10
release-management10
geo10
automation10
rapids10
ci-cd-pipeline10
Programming languages (12)
Github contributions (5)
rapidsai/cuspatial
Sep 2019 - Jan 2023
CUDA-accelerated GIS and spatiotemporal algorithms
Role in this project:
Back-end Developer
Back-end DeveloperContributions:12 releases, 65 reviews, 89 commits in 3 years 4 months
Contributions summary:Ray primarily contributed to the backend functionality of the `cuspatial` repository, focusing on CUDA-accelerated spatial algorithms. Their work included modifying the shapefile reader, contributing to cython bindings, and creating utility functions for spatial operations. The user's changes indicate involvement in developing and maintaining core components for processing and analyzing geospatial data using CUDA. They were involved in tasks such as reading shapefile data, implementing spatial algorithms, and creating performance improvements.
cudagis
rapidsai/cugraph
Dec 2018 - Jan 2023
cuGraph - RAPIDS Graph Analytics Library
Role in this project:DevOps Engineer & Build Automation Engineer
DevOps Engineer & Build Automation EngineerContributions:15 releases, 151 reviews, 171 commits in 4 years 2 months
Contributions summary:Ray primarily focused on automating and improving the continuous integration and continuous deployment (CI/CD) pipeline for the cuGraph project. They implemented and modified scripts for building conda packages, including `conda_build.sh`, and `ci/cpu/*` scripts. Furthermore, they updated the build processes, including setting up project flash and ensuring the correct environment variables are set, demonstrating a strong understanding of build automation and deployment strategies.
graphrapidsnvidiagpucuda
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