Sachin Sanap
Sr. Systems SW Engineer at NVIDIA
Durham, North Carolina, United States
Summary
🤩
Rockstar🎓
Top SchoolSachin Sanap is a senior systems software engineer with over 8 years of experience building and operating cloud-scale storage and GPU platform infrastructure, currently focused on NVIDIA’s GPU Cloud Platform for gaming and AI/DL workloads. He brings deep domain expertise in data storage, cloud platforms, and web-scale systems from roles at NetApp, HPE SimpliVity, and earlier storage-focused engineering, combining hands-on development with team leadership and a knack for breaking down internal silos to deliver results. Sachin has practical DevOps and IaC experience—demonstrated by automating deployment and orchestration for NVIDIA’s AIStore on AWS using Ansible and cluster automation for perf testing. Colleagues rely on him to advocate engineering efficiencies and turn complex storage problems into operational solutions. Based in Durham, NC, he pairs a strong academic foundation (MS Computer Science, University of Florida) with a pragmatic, systems-first approach to scaling AI and storage systems.
8 years of coding experience
11 years of employment as a software developer
Savitribai Phule Pune UniversityMS, Computer Science, MS, Computer Science at University of Florida
Github Skills (12)
ansible-galaxy10
bash10
infrastructure10
cloud-infrastructure10
aws10
devops10
ansible10
kubernetes-pods9
automation9
automations9
kubernetes9
object-storage8
Programming languages (1)
Github contributions (1)
NVIDIA/aistore
Apr 2018 - Feb 2019
AIStore: scalable storage for AI applications
Role in this project:
DevOps Engineer
DevOps EngineerContributions:79 commits, 186 pushes, 2 branches in 10 months
Contributions summary:Sachin primarily focused on automating the deployment and management of the AIStore application on an AWS infrastructure. Their contributions involved creating and modifying Ansible scripts for setting up, configuring, and starting the AIStore cluster. They also worked on scripts to manage volumes, collect logs, and deploy the application, showcasing expertise in infrastructure-as-code and cluster orchestration. The user also appears to have worked on setting up the perf test environment.
scalabledeep-learningdistributed-shufflemachine-learninghyper-converged
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