Kristopher Siman
Senior System Development Engineer at Amazon Web Services (AWS)
New York City Metropolitan Area United States
Summary
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RockstarKristopher Siman is a Senior System Development Engineer with nine years of hands-on experience building and automating scalable infrastructure, currently at AWS in the New York City area. He has deep DevOps and automation expertise from progressive roles at smartTrade Technologies and Ingenu, advancing from lab technician to technical lead and system development engineer. At AWS he contributes to high-impact open-source work maintaining AWS Deep Learning Containers, focusing on PyTorch and MXNet integrations, CUDA compatibility, and security-driven patching. Kristopher combines practical systems integration and test experience with production-grade container and CI/CD maintenance, making him adept at bridging development and operational needs. He brings a pragmatic approach to problem solving—tackling thorny testing and AMI integration issues that often go unnoticed but critically affect reliability. Colleagues value his steady progression and ability to deliver robust automation under operational constraints.
9 years of coding experience
8 years of employment as a software developer
Github Skills (11)
cd10
pytorch10
docker10
aws10
dockers10
cicd10
python9
mxnet9
tensorflow8
kubernetes-pods7
kubernetes7
Programming languages (3)
Github contributions (5)
aws/deep-learning-containers
Sep 2022 - Dec 2022
AWS Deep Learning Containers are pre-built Docker images that make it easier to run popular deep learning frameworks and tools on AWS.
Role in this project:
DevOps Engineer
DevOps EngineerContributions:2 releases, 190 reviews, 35 commits in 2 months
Contributions summary:Kristopher primarily focuses on maintaining and patching the AWS Deep Learning Containers, with a strong emphasis on PyTorch and MXNet versions. Their contributions include updating Dockerfiles, build specifications, and test configurations to integrate new versions of deep learning frameworks (PyTorch 1.12, MXNet 1.9), and address security issues. The user also works on adapting the container images to use different CUDA versions. They also implement changes to accommodate new AMI releases and related fixes for the testing infrastructure.
pytorchsagemakercontainersmxnetserving
kace/deep-learning-containers
Sep 2022 - Mar 2025
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.
Contributions:1 release, 211 pushes, 87 branches in 2 years 6 months
containerspytorchmxnetservingdeep-learning
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