Bharani Kempaiah
Software Development Engineer II at Amazon Web Services (AWS)
Seattle, Washington, United States
Summary
👤
Senior🎓
Top SchoolBharani Kempaiah is a Software Development Engineer II with 8 years of experience crafting scalable cloud-native systems and ML-driven features, currently improving EKS control-plane scalability and reducing scaling latency by up to 60% at AWS. A Carnegie Mellon MCDS graduate, he blends data science and ML expertise with production engineering—having contributed ASR configuration and self-supervised training work to the well-known ESPnet speech toolkit. His background spans end-to-end ML deployment (GCP Vertex, TorchServe), CI/CD automation, and practical model engineering (e.g., CNN cattle-weight prediction with 9% MAPE), plus research stints that inform his system-focused approach. Based in Seattle, he pairs deep academic training with hands-on performance optimization and has a knack for surfacing non-obvious signals—like using network utilization to improve autoscaling on large clusters—to drive measurable operational and cost improvements.
8 years of coding experience
2 years of employment as a software developer
Bachelor of Technology - BTech Computer Science, Bachelor of Technology - BTech Computer Science at PES UniversityMaster of Computational Data Science, Master of Computational Data Science at Carnegie Mellon UniversityDelhi Public School Bangalore North
Github Skills (14)
pytorch10
deeplearning-ai10
speech-recognition10
deep-learning10
scripting9
python9
conformer9
shell9
dataprep9
data-preprocessing9
script9
sh9
machine-translation6
text-to-speech4
Programming languages (1)
Github contributions (5)
espnet/espnet
Mar 2022 - Apr 2022
End-to-End Speech Processing Toolkit
Role in this project:
ML Engineer
ML EngineerContributions:3 reviews, 23 commits, 1 PR in 24 days
Contributions summary:Bharani primarily focused on modifying configuration files and scripts related to Automatic Speech Recognition (ASR) within the ESPnet toolkit. Their contributions included adding and updating configurations for Self Supervised Training using HuBERT, SpecAugment, and Conformer models, specifically for the "ml_openslr63" language dataset. They also updated data preparation scripts to adjust test set sizes. These changes indicate a focus on model training and potentially dataset optimization.
speech-recognitionspeech-separationchainerspoken-language-understandingspeech-processing
bharaniuk/bharaniuk.github.io
May 2022 - Jul 2023
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Contributions:172 pushes in 1 year 1 month
tailwindcsssimple-websiteminutes
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