Aravind Segu
Software Engineer at Databricks
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAravind Segu is a software engineer with nine years of hands-on experience building AI infrastructure and GenAI agents at Databricks and Meta, currently based in San Francisco. He brings production-grade expertise in distributed systems, data pipelines, and cloud infrastructure—having optimized anomaly-detection storage at Facebook and built forecasting pipelines with Airflow and Terraform at Databricks. As an active open-source contributor, he improved MLflow’s LangChain integration by enabling relative model paths and dependency support, reflecting a practical focus on ML lifecycle usability. His internships at Apple, DRW, and others show a consistent pattern of delivering measurable performance gains—CPU reductions, faster queries, and storage efficiencies. A University of Waterloo software engineering student-turned-professional, he pairs entrepreneurial ambition with a proven ability to turn research and prototypes into scalable systems. He’s comfortable at the intersection of ML, infra, and product, often translating data-science needs into robust engineering solutions.
9 years of coding experience
4 years of employment as a software developer
Henry Wise Wood High SchoolBachelors of Software Engineering Computer Software Engineering, Bachelors of Software Engineering Computer Software Engineering at University of Waterloo
English, Telugu, Hindi, Tamil
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (7)
machine-learning10
model-management10
mlflow10
python10
langchain10
ai9
pytest8
Programming languages (6)
Github contributions (5)
mlflow/mlflow
May 2024 - Feb 2025
Open source platform for the machine learning lifecycle
Role in this project:
ML Engineer
ML EngineerContributions:21 reviews, 14 PRs, 20 comments in 8 months
Contributions summary:Aravind primarily contributed to the `mlflow/mlflow` repository, which is a machine learning lifecycle platform. Their commits focus on enhancing the functionality of logging and managing machine learning models, specifically within the context of the Langchain framework. The code changes involve allowing relative paths in model logging, modifying existing tests to accommodate new features, and adding dependency support, thereby improving the integration capabilities of MLflow with Langchain. These contributions suggest a focus on expanding the platform's usability for ML workflows.
pythonlifecyclemlmachine-learningincremental-learning
aravind-segu/databricks-sdk-py
Sep 2024 - Mar 2025
Databricks SDK for Python (Beta)
Contributions:59 pushes, 5 branches in 5 months
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