Adhita Selvaraj
Software Engineer, AI ML - AI Platform
San Francisco, California, India
Summary
👤
Senior🎓
Top SchoolAdhita Selvaraj is a software engineer specializing in AI/ML platform engineering with nine years of experience building cloud-native ML infrastructure and orchestration on Kubernetes. Based in San Francisco, she has been a key contributor and tech lead on Kubeflow and related projects—improving the Central UI, integrating Argo dashboards, and enabling Ambassador gateway mappings—while driving hybrid cloud ML pipeline prototypes (on-prem → SageMaker/GCP). She led development of dl-kops, a one-click Kubernetes + CUDA + Nvidia driver installer and ML job portal, and contributed scheduling research for fair GPU allocation via kube-arbitrator. Comfortable across Go, Python, and Swift, she blends production DevOps and full-stack UI work with research-driven solutions, and participates in the MLPerf inference working group. Her background spans startups and enterprise teams (Cisco, Narvar, Addepar), where she focuses on observability and scalable agent-based AI platform features.
9 years of coding experience
7 years of employment as a software developer
Bachelor of Engineering Electronics and Communication, Bachelor of Engineering Electronics and Communication at Anna University ChennaiMaster’s Degree Electrical Engineering, Master’s Degree Electrical Engineering at Stony Brook University
English, Tamil, Hindi, French
Github Skills (12)
kubeflow10
kubernetes10
docker10
dockers10
cicd10
kubernetes-pods10
go9
yaml8
front-end-development8
css7
javascript7
machine-learning6
Programming languages (13)
Github contributions (5)
kubeflow/kubeflow
Mar 2018 - Oct 2019
Machine Learning Toolkit for Kubernetes
Role in this project:
Full-stack & DevOps Engineer
Full-stack & DevOps EngineerContributions:30 commits, 52 PRs, 392 comments in 1 year 7 months
Contributions summary:Adhita's contributions primarily centered around enhancing the Central UI for Kubeflow. They implemented integration with the Ambassador API gateway, including defining mappings. They also worked on building a Docker image for the Central UI and integrated it into the release pipeline. The user added the Argo UI dashboard to the UI and addressed various frontend issues, including fixing layout problems.
pythondata-sciencenotebookmachine-learningmlops
swiftdiaries/rust-grpc-example
Sep 2020 - May 2022
A minimal Rust based gRPC client and server (using tonic-rs)
Contributions:1 review, 5 commits, 1 PR in 1 year 7 months
rustgrpc-clientgrpctonic
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