Aditya Pola
AWS DevOps Chef Engineer at Capital One
Minneapolis, Minnesota, United States
Summary
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RockstarAditya Pola is an AWS DevOps and Chef engineer with six years of hands-on experience building and automating cloud infrastructure for large enterprises like Capital One and Target, focused on configuration management, CI/CD, and observability. He has deep practical experience with Chef, Jenkins (Job-DSL/Groovy), and automating stacks that include Java, Gradle, Python, Hadoop components, RabbitMQ, and MongoDB, and has applied orchestration tools such as Heat for OpenStack. Beyond operations, he contributes to open-source projects in ML and reinforcement learning—improving core data-type and normalization logic in the Ivy framework and strengthening test automation and CI for the widely used PettingZoo library—showing a blend of infrastructure rigor and interest in ML tooling. Based in Minneapolis, he pairs enterprise-grade automation skills with a pragmatic approach to keeping CI/CD and testing dependencies current, making complex production environments more reliable and maintainable.
7 years of coding experience
Github Skills (16)
ivy10
machine-learning10
pytest10
python10
cicd10
testing10
neural-network9
isort8
flake8
pytorch8
numpy8
tensorflow8
reinforcement-learning7
converter7
deeplearning-ai7
Programming languages (6)
Github contributions (5)
Farama-Foundation/PettingZoo
Apr 2022 - Apr 2022
A standard API for multi-agent reinforcement learning environments, with popular reference environments and related utilities
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:10 commits, 2 PRs in 2 days
Contributions summary:Aditya's contributions primarily focused on improving the testing infrastructure and ensuring the quality of the `pettingzoo` project. This involved resolving dependency issues related to testing tools like `pytest` and `codespell`, as well as updating the project's setup.py file to include necessary testing dependencies and configuring the CI/CD pipeline. Further contributions focused on bringing the repository up-to-date and removing testing dependencies.
multi-agent-reinforcement-learningapigymnasiumreinforcement-learningmultiagent-reinforcement-learning
unifyai/ivy
Aug 2022 - Dec 2022
Convert Machine Learning Code Between Frameworks
Role in this project:ML Engineer
ML EngineerContributions:16 commits, 33 PRs, 21 pushes in 4 months
Contributions summary:Aditya contributed to the `ivy-llc/ivy` repository, which focuses on converting machine learning code between frameworks. Their commits primarily involved modifications to the `data_type.py`, `data_type.py`, and `norms.py` files, indicating a focus on core functionality related to data type handling and layer normalization within the Ivy framework. Additionally, the user made updates related to the `smooth_l1_loss` function and docstring fixes, suggesting an involvement in improving the framework's capabilities and usability.
machine-learningpythontensorflowpytorchnumpy
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