Prachi Gandhi
Software Engineer at Oracle
United States
Summary
🤩
Rockstar🎓
Top SchoolPrachi Gandhi is a software engineer with five years of experience building cloud-native infrastructure and AI services, currently focused at Oracle after contributing to Azure AI Responsible AI and Evaluations at Microsoft. She specializes in Kubernetes autoscaling, foundation models, response generation, evaluations, and prompt engineering, and has hands-on expertise optimizing cluster-autoscaler behavior and secure node bootstrapping. Prachi’s open-source contributions to the prominent kubernetes/autoscaler repo improved Azure VMSS handling and reduced SKU API load, reflecting a pragmatic focus on performance and cloud provider integration. As a tech lead she introduced partial scaling to improve cluster utilization and implemented release and automation practices that smoothed delivery cycles. Her background blending VMware kernel- and VM-lifecycle work with fog computing research gives her a rare cross-layer perspective from device and VM orchestration up to AI model deployments.
5 years of coding experience
6 years of employment as a software developer
Master of Science (MS) Electrical and Computer Engineering Systems and Software, Master of Science (MS) Electrical and Computer Engineering Systems and Software at Georgia Institute of TechnologyBachelor of Engineering (B.E) Electronics and Telecommunications, Bachelor of Engineering (B.E) Electronics and Telecommunications at University of Mumbai
English, Gujarati, Hindi, Marathi
Github Skills (12)
kubernetes10
azure10
go10
cloud-services10
microsoft-azure10
cloud-hosting10
cloud-computing10
gcloud10
kubernetes-pods10
api8
apidoc8
testing7
Programming languages (4)
Github contributions (5)
kubernetes/autoscaler
Feb 2022 - Sep 2022
Autoscaling components for Kubernetes
Role in this project:
Backend & Cloud Engineer
Backend & Cloud EngineerContributions:16 reviews, 8 commits, 26 PRs in 7 months
Contributions summary:Prachi contributed to the `kubernetes/autoscaler` repository by fixing bugs related to Azure instance metadata, adding fields for Azure instance types, and supporting dynamic SKUs for scaling. They also updated the cloud provider azure version and made modifications to improve the logic for fetching VMSS information, especially for Spot and Uniform VMSS. The contributions indicate a focus on Azure cloud integration and performance optimizations.
k8skubernetesautoscaling
gandhipr/autoscaler
Dec 2021 - Jul 2024
Autoscaling components for Kubernetes
Contributions:72 pushes, 30 branches in 2 years 7 months
hands-onupstreamautoscalingk8skubernetes
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.