Lavanya Kanchanapalli
Engineer Manager, ML Infrastructure at Moloco
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolLavanya Kanchanapalli is an engineering manager with 10 years of experience building and scaling ML infrastructure and low-latency model-serving systems across top tech companies. Based in the San Francisco Bay Area, she has led teams at Moloco, Walmart, Twitter, and Netflix to deliver production ML pipelines, feature stores, and GPU-backed real-time inference platforms using technologies like TensorFlow, Triton, BigQuery, Airflow, and Terraform. She combines hands-on backend expertise (Java/Scala/Python) with strong operational and hiring practices, driving measurable outcomes such as multi-million dollar revenue impact from real-time embedding models and significant reliability improvements in cloud CI/CD workflows. A contributor to Netflix’s Hollow project, she has practical experience building data pub-sub and validation tooling that prevents production data incidents. Known for mentoring, cross-team collaboration, and translating research-grade ML into robust, observable infrastructure, she balances technical depth with people and process leadership.
10 years of coding experience
16 years of employment as a software developer
Jawaharlal Nehru Technological University HyderabadMS Computer Science, MS Computer Science at George Mason University
Github Skills (5)
unit-testing10
javas10
validation10
validations10
java10
Programming languages (1)
Github contributions (1)
Netflix/hollow
Feb 2016 - Mar 2019
Hollow is a java library and toolset for disseminating in-memory datasets from a single producer to many consumers for high performance read-only access.
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:6 releases, 173 commits, 16 PRs in 3 years 1 month
Contributions summary:Lavanya implemented and tested a duplicate data detection validator for the Hollow library. They created initial versions and iterated on the validator's functionality, including moving it to the correct package and adding unit tests. The user also added a record count variance validator and made subsequent adjustments to the error messages and the logic within the record count validation.
memoryjava-librarydatasetbig-dataconsumers
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