Mani Parkhe
Sr. Staff Software Engineer at Databricks
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolMani Parkhe is a Sr. Staff Software Engineer in San Francisco with 11 years of experience building scalable backend and ML/AI platform systems, currently at Databricks. He was a founding member and tech lead for key MLflow components—including Experiment Tracking and the Model Registry—and led the Feature Store from ideation to Public Preview, blending product-grade APIs with robust data models. Prior roles at Uber and LinkedIn honed his expertise in high-QPS data systems, communications platforms, fraud and pricing pipelines, and large-scale analytics engines. Mani contributes to the prominent open-source MLflow project, focusing on backend and MLOps work such as model lifecycle APIs and registry deletion/restore workflows. He combines deep systems architecture experience from EDA and datacenter-scale services with a knack for turning customer needs into production-ready platform features. His background in electrical engineering and an MS in computer science underpin a pragmatic, performance-oriented approach to complex ML infrastructure.
11 years of coding experience
19 years of employment as a software developer
Fergusson CollegeBE, Electrical Engineering, BE, Electrical Engineering at College of Engineering PuneMS, Computer Science, MS, Computer Science at University of Florida
Github Skills (16)
rest-api10
mlflow10
api10
sqlalchemy10
python10
back-end-development10
apidoc10
model-registry10
ml9
machine-learning9
experiment9
experiment-manager9
database-design8
sql7
dockers7
Programming languages (3)
Github contributions (5)
mlflow/mlflow
Jun 2018 - Jun 2020
Open source platform for the machine learning lifecycle
Role in this project:
Back-end Developer & MLOps Engineer
Back-end Developer & MLOps EngineerContributions:1 release, 9 reviews, 108 commits in 2 years
Contributions summary:Mani primarily focused on back-end development and MLOps tasks, contributing to the core infrastructure of the MLflow project. Their commits indicate a focus on improving APIs for experiment management and Model Registry, including deletion and restoration functionalities. The user also implemented and maintained supporting components of the tracking API, and made changes to the REST API, indicating an understanding of the project's architecture and requirements for reliable model management.
pythonlifecyclemlmachine-learningincremental-learning
mparkhe/mlflow
Jun 2018 - May 2024
Open source platform for the complete machine learning lifecycle
Contributions:220 pushes, 203 branches in 6 years
pythonlifecyclemlmachine-learningincremental-learning
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.