Animesh Singh
Senior Director, AI Platform And Infrastrcuture
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolAnimesh Singh is a seasoned AI and cloud-native leader with over a decade of experience building production-grade ML platforms and infrastructure, currently serving as Senior Director of AI Platform and Infrastructure at LinkedIn. He combines enterprise leadership (former IBM Distinguished Engineer and Watson CTO-level roles) with deep hands-on open-source contributions, including co-founding KServe and work on Kubeflow Pipelines to enable serverless ML inference on Kubernetes. Known for bridging trusted AI, MLOps, and cloud-native primitives like Kubernetes, Istio, and KNative, he has driven integrations that make scikit-learn, TensorFlow and PyTorch models deployable and monitorable at scale. Active in industry governance as a former LFAI Trusted AI chair and CNCF ambassador, he brings both technical depth and ecosystem stewardship to large-scale AI delivery.
10 years of coding experience
9 years of employment as a software developer
MS Computer Software Engineering, MS Computer Software Engineering at The University of Texas at Dallas
Hindi
Github Skills (14)
scikit-learn10
kubeflow10
kubernetes10
machine-learning10
mlops10
tensorflow10
python10
kubernetes-pods10
scikit10
dockers9
docker9
pytorch9
cicd8
xgboost4
Programming languages (17)
Github contributions (5)
kserve/kserve
Apr 2019 - Oct 2021
Standardized Serverless ML Inference Platform on Kubernetes
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 35 reviews, 106 commits in 2 years 6 months
Contributions summary:Animesh primarily contributed to the integration and support of the Scikit-learn framework within the KServe platform. Their work included creating a model server implementation for Scikit-learn, which involved defining model loading, pre/post-processing, and prediction functionalities. They also refactored the code based on review comments, added testing capabilities, and wired the Scikit-learn server to the KFService. The user's contributions enabled the deployment of Scikit-learn models on Kubernetes.
xgboostsklearnserverlessclient-goknative
kubeflow/pipelines
Feb 2019 - Feb 2021
Machine Learning Pipelines for Kubeflow
Role in this project:MLOps Engineer
MLOps EngineerContributions:14 reviews, 26 commits, 31 PRs in 2 years
Contributions summary:Animesh's commits primarily involve setting up and configuring machine learning pipelines for Kubeflow. They are making changes to components that deploy and manage machine learning models using Seldon, including modifying the code to deploy models based on TensorFlow and PyTorch. The commits also involve adapting existing pipelines to use components for Watson OpenScale, managing model monitoring, and configuring quality metrics. The user is also modifying the KFServing deployment.
pipelinetektondata-sciencemachine-learningmlops
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.