Arjun Dcunha
Software Engineer at Databricks
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolArjun Dcunha is a software engineer based in San Francisco with six years of experience building production-grade ML systems and tooling. He holds a B.Tech with honors from IIT Hyderabad and is completing an MS at Columbia specializing in machine learning and computer vision. At Databricks he contributes to MLflow, improving model registry and multi-cloud artifact management, and has added support for tensor data and PyTorch logging examples—work that directly impacts ML lifecycle reliability at scale. His background includes internships at Google Brain and contributions to face-alignment research, as well as teaching roles that reflect strong mentorship in deep learning curricula. Comfortable bridging research and production, he has a track record of shipping robust, scalable pipelines and integrating academic ideas into enterprise ML platforms. Colleagues describe him as a detail-oriented engineer who blends deep technical rigor with practical deployment focus.
6 years of coding experience
Bachelor of Technology - BTech, Computer Science Engineering, Bachelor of Technology - BTech, Computer Science Engineering at Indian Institute of Technology, HyderabadMasters, Computer Science, Masters, Computer Science at Columbia University in the City of New York
Github Skills (15)
mlflow10
python10
machine-learning9
aws8
microsoft-azure8
azure8
pytorch7
javas6
data-pipelines6
tensorflow6
data-pipeline6
java6
api4
apache-spark4
api-design4
Programming languages (2)
Github contributions (5)
mlflow/mlflow
Apr 2020 - Sep 2022
Open source platform for the machine learning lifecycle
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 74 reviews, 16 commits in 2 years 5 months
Contributions summary:Arjun's commits primarily focus on enhancing the MLflow client with features and fixes related to model management and artifact storage, especially in the context of Databricks environments. Their work involved integrating the Databricks model registry, improving artifact repository functionalities for Azure, AWS, and Databricks, and adding support for multi-dimensional array/tensor data types. They also contributed to examples demonstrating model logging and scoring with PyTorch.
pythonlifecyclemlmachine-learningincremental-learning
arjundc-db/mlflow
Mar 2020 - Apr 2023
Open source platform for the machine learning lifecycle
Contributions:3 PRs, 135 pushes, 28 branches in 3 years 1 month
pythonlifecyclemachine-learningdvcmachine-learning-lifecycle
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