Mugdha Hardikar
Associate AI Architect
Pune, Maharashtra, India
Summary
🤩
Rockstar🎓
Top SchoolMugdha Hardikar is an Associate AI Architect with four years focused on deep learning and a decade-plus engineering background that bridges software development, DevOps, and applied ML. She has led projects across audio denoising, computer vision, and time-series forecasting, and built "Inference Bridge" to port scikit-learn models into diverse application stacks (Java, Python, Swift). A silver medalist in the Kaggle HPA competition, she combines hands-on modeling with production-grade deployment skills, having contributed to DataHub integrations that improved Spark lineage ingestion and performance. Her experience spans roles from software engineer to lead data scientist, enabling her to translate research models into reliable, observable pipelines. Based in Pune, she pairs a US master's in computer science with pragmatic open-source contributions and a track record of operationalizing ML in heterogeneous environments.
4 years of coding experience
12 years of employment as a software developer
Master's degree Computer Science, Master's degree Computer Science at University of North Carolina at CharlotteBachelor of Engineering (B.E.) Computer Science, Bachelor of Engineering (B.E.) Computer Science at Smt. kashibai navale college of engineering
English, Marathi, Hindi
Github Skills (12)
javas10
spark10
metadata10
data-pipelines10
data-pipeline10
java10
datahub10
data-engineering10
devops9
jtest9
junit9
rest-api8
Programming languages (4)
Github contributions (5)
datahub-project/datahub
Dec 2021 - Sep 2022
The Metadata Platform for your Data and AI Stack
Role in this project:
Back-end Developer & DevOps Engineer
Back-end Developer & DevOps EngineerContributions:28 reviews, 36 commits, 41 PRs in 9 months
Contributions summary:Mugdha implemented core features related to pushing data lineage from Spark to DataHub, including the ability to push lineage from Spark to DataHub's metadata platform. Their contributions involved modifying test code and integrating with a mock server. The user also refactored Spark lineage configurations for better parsing, enhancing logging, and documentation. Additionally, the user added support for the persist API and implemented a feature to coalesce Spark jobs, likely improving performance.
data-managementdata-discoverydata-stackmodern-data-stackdata-catalog
MugdhaHardikar-GSLab/datahub
Jan 2022 - Mar 2022
The Metadata Platform for the Modern Data Stack
Contributions:5 PRs, 98 pushes, 29 branches in 1 month
data-managementdata-stackmodern-data-stackmetadata-managementdata-catalog
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