Gautam Kumar
Senior Solutions Architect at Amazon Web Services (AWS)
Gurugram, Haryana, India
Summary
🤩
Rockstar🎓
Top SchoolGautam Kumar is a Senior Solutions Architect at AWS in Gurugram with 12 years of experience designing and operationalizing cloud-native solutions across enterprise and DevOps contexts. He progressed through hands-on roles from programmer analyst and ETD/intellimatch developer to cloud DevOps and AWS-focused architecture, bringing deep practical experience in reliability, automation, and deployment pipelines. Gautam contributes to open-source ML projects—helping stabilize MXNet tests and integrating newer PyTorch releases into the Deep Java Library—reflecting a pragmatic focus on compatibility and production readiness. Known for bridging developer workflows and infrastructure concerns, he excels at translating complex native-library and CUDA challenges into reproducible build and deployment solutions.
12 years of coding experience
14 years of employment as a software developer
High School/Secondary Certificate Programs, High School/Secondary Certificate Programs at Sharons Public School+2 PCMB, +2 PCMB at K.V.Bailey Road,Patna
English, Hindi
Github Skills (17)
pytorch10
python10
testing10
mxnet10
test-automation10
java9
machine-learning9
deeplearning-ai9
deep-learning9
javas9
cuda9
ai8
ml8
java-native-interface7
dockers7
Programming languages (18)
Github contributions (5)
apache/mxnet
Sep 2017 - Jan 2018
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:11 commits, 34 PRs, 94 comments in 4 months
Contributions summary:Gautam primarily focused on improving the quality and stability of the `mxnet` project. Their commits involved disabling and temporarily skipping failing tests, particularly those related to input/output operations and batch normalization, which were causing intermittent failures on Windows. The changes included updates to existing test files and modifying test execution with skip annotations to prevent failures. They also adjusted tolerance thresholds to accommodate differences in calculations.
pythonschedulerdataflowmutationdata-science
deepjavalibrary/djl
Feb 2021 - Apr 2021
An Engine-Agnostic Deep Learning Framework in Java
Role in this project:MLOps Engineer
MLOps EngineerContributions:32 reviews, 8 commits, 12 PRs in 1 month
Contributions summary:Gautam primarily focused on integrating and updating PyTorch within the DJL framework. This involved upgrading the PyTorch version, addressing native library downloads and builds, and integrating CUDA support. Additionally, the user made changes to the build scripts and configuration files to accommodate the PyTorch updates, and also updated versions in relevant files. These contributions helped maintain compatibility of DJL with different versions of PyTorch and associated dependencies.
pytorchmxnetcaffe2deep-learningagnostic
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