Ken Hester
Director, Solutions Architects at NVIDIA
Houston, Texas, United States
Summary
👤
Senior🎓
Top SchoolKen Hester is a seasoned technical leader with over 10 years in solutions architecture and more than two decades in software engineering, currently directing Global HPC & AI Solutions Architecture for NVIDIA's Energy practice from Houston. He blends hands-on expertise in CUDA, HPC, and accelerated AI stacks with strategic management—designing and sometimes implementing large-scale on-prem and cloud GPU solutions that drive customer outcomes. Prior roles span principal architect work optimizing CUDA/OpenCL workflows and early leadership building enterprise software at Halliburton and Dell, giving him deep domain knowledge in energy and systems software. He has practical DevOps and performance engineering experience in scientific compute projects (contributing CI and container improvements to a finite-difference DSL/compiler repo), highlighting his focus on reproducible high-performance deployments. With an MBA in Finance and a BS in Computer Engineering, he bridges technical depth and commercial strategy to align cutting-edge compute platforms with business needs.
10 years of coding experience
27 years of employment as a software developer
MBA Finance, MBA Finance at University of HoustonBachelor of Science - BS Computer Engineering, Bachelor of Science - BS Computer Engineering at Texas A&M University
Github Skills (12)
cuda10
openmp10
docker10
performance-monitor10
performance-analysis10
gpu10
dockers10
cicd10
openacc9
compiler-compiler9
hpc9
compiler9
Programming languages (5)
Github contributions (5)
devitocodes/devito
Jun 2020 - Apr 2022
DSL and compiler framework for automated finite-differences and stencil computation
Role in this project:
DevOps Engineer & Performance Engineer
DevOps Engineer & Performance EngineerContributions:2 reviews, 65 commits, 15 PRs in 1 year 11 months
Contributions summary:Ken primarily focused on improving the build and deployment processes. They updated container configurations to support different compiler setups, specifically PGI and CUDA. Additionally, the user contributed to performance testing and validation by disabling CUDA-aware Dask in the CI pipeline and later enabling it to test and validate performance with different options.
code-generationautomatic-differentiationfinite-differencecompilerultrasound-imaging
kenhester/devito
Jun 2020 - Nov 2022
Code generation framework for automated finite difference computation
Contributions:58 pushes in 2 years 5 months
computationcode-generationpythonfinite-differencefinite
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.