Valay Dave
Staff Software Engineer at Anaconda, Inc.
San Francisco, California, United States
Summary
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Rockstar🎓
Top SchoolValay Dave is a software engineer with 11 years of experience building resilient backend and cloud infrastructure, currently based in San Francisco and contributing at Outerbounds. He has deep hands-on experience in productionizing AI/ML workflows, including notable backend and DevOps contributions to Netflix’s open-source Metaflow—improving exception handling, storage consistency, and Azure support. His background spans roles from technical architect to cloud infrastructure intern, reflecting a strong practical blend of system design, data integrity, and deployment automation. Valay holds a master's in computer science from Arizona State and began his career delivering full-stack solutions in fast-paced startups. He also pursues side projects like sci-genie.com, showing a continuing interest in applied research tooling and product-focused engineering. Pragmatic and detail-oriented, he excels at turning brittle distributed systems into robust, maintainable platforms.
11 years of coding experience
8 years of employment as a software developer
Bachelor's Degree Information Technology, Bachelor's Degree Information Technology at SRM UniversityMaster's degree Computer Science, Master's degree Computer Science at Ira A. Fulton Schools of Engineering at Arizona State University
Stackoverflow
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Github Skills (14)
data-storage10
error-handling10
python10
back-end-development10
microsoft-azure9
azure9
mlops9
data-pipeline8
data-pipelines8
machine-learning7
aws6
kubernetes-pods5
gcp5
kubernetes5
Programming languages (12)
Github contributions (5)
Netflix/metaflow
Sep 2021 - Jan 2023
Build, Manage and Deploy AI/ML Systems
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:2 releases, 243 reviews, 88 commits in 1 year 5 months
Contributions summary:Valay primarily contributed to the backend infrastructure and data storage aspects of the Metaflow project. Their work involved addressing exception handling in task execution and error representation, and also fixing inconsistent behavior within the data storage system. Further contributions included the implementation of a decorator hook to add custom packages, and modifications to handle exceptions with non-picklable artifacts, indicating a focus on system robustness and data integrity. Finally, the user's commits touch upon Azure cloud support.
aimachine-learningmodel-managementmlml-platform
outerbounds/metaflow-deepspeed
Mar 2024 - Oct 2024
Contributions:2 releases, 7 reviews, 15 PRs in 7 months
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