Vamshi Teja
Machine Learning Engineer at Atlassian
Hyderabad, Telangana, India
Summary
👤
Senior🎓
Top SchoolVamshi Teja is a Machine Learning Engineer with a decade of experience building production ML and search systems, currently at Atlassian after leading ML/Search efforts at Mercari. He combines strong academic pedigree—MS in Computer Science from UIUC (4.0 GPA) and a BTech from IIT Hyderabad—with hands-on engineering across model development, deployment and MLOps. His open-source contributions include Kubernetes deployment integrations for Optuna, reflecting practical expertise in containerized hyperparameter tuning and CI/CD for ML. Comfortable spanning research and production, he brings a knack for translating experimental ideas into robust, version-compatible pipelines that scale in cloud-native environments.
10 years of coding experience
2 years of employment as a software developer
Bachelor of Technology (BTech), Electrical Engineering and Computer Science Engineering as Second Major, 9.17, Bachelor of Technology (BTech), Electrical Engineering and Computer Science Engineering as Second Major, 9.17 at Indian Institute of Technology, HyderabadMaster of Science - MS, Computer Science, 4.0/4.0, Master of Science - MS, Computer Science, 4.0/4.0 at University of Illinois Urbana-Champaign
English, Telugu, Hindi
Stackoverflow
1reputation
0reached
0answers
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Github Skills (13)
sh10
kubernetes10
script10
docker10
shell10
dockers10
scripting10
kubernetes-pods10
python9
machine-learning8
distribute7
hyperparameter-optimization7
parallel7
Programming languages (3)
Github contributions (5)
optuna/optuna
Aug 2020 - Aug 2020
A hyperparameter optimization framework
Role in this project:
DevOps Engineer
DevOps EngineerContributions:11 commits, 2 PRs, 5 comments in 20 days
Contributions summary:Vamshi's primary contribution involves setting up and configuring examples related to Kubernetes deployments within the Optuna framework. This includes writing shell scripts to build Docker images and deploy examples to a Kubernetes cluster using `kubectl`. Further contributions include integrating existing examples with kubernetes configurations and merging changes related to the deployments. Additionally, the user has been responsible for ensuring version compatibility and has made several updates to integrate testing and examples, including refactoring imports.
pythonoptimization-frameworkparallelhyperparameteroptimization
merumaru/marumaru-frontend
Nov 2019 - Mar 2023
UI that's easy to use
Contributions:2 PRs, 2 pushes, 1 branch in 3 years 4 months
react
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