Purnendu Mukherjee
California, United States
Summary
🤩
Rockstar🎓
Top SchoolPurnendu Mukherjee is an experienced software engineer with 10 years in the field, blending deep academic training (Master’s degrees from University of Calcutta and University of Florida) with hands-on development across Python, SQL, JavaScript, and deep learning frameworks. Based in California, he has a strong research background and a track record of improving data processing and integrations in large-scale open-source projects. Notably, he contributed backend enhancements to NVIDIA NeMo—expanding data import capabilities for DialogFlow and tightening code quality with PEP8 and utility improvements. He excels at bridging research-grade models and production data pipelines, turning complex ML workflows into maintainable code. Colleagues value his mix of meticulous code style discipline and practical system-level thinking when integrating external services.
10 years of coding experience
Master’s Degree, Computer Science, First Class with Honors, Master’s Degree, Computer Science, First Class with Honors at University of CalcuttaMaster’s Degree, Computer Science, Master’s Degree, Computer Science at University of Florida
Hindi, Bengali, English
Github Skills (8)
python10
data-processing10
asr9
artificial-neural-networks9
neural-network9
deep-learning9
generative-ai8
large-language-models8
Programming languages (6)
Github contributions (5)
NVIDIA/NeMo
Oct 2019 - Nov 2021
A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
Role in this project:
Back-end Developer
Back-end DeveloperContributions:11 reviews, 32 commits, 6 PRs in 2 years 1 month
Contributions summary:Purnendu primarily focused on improving and expanding the data processing capabilities within the `nemo` repository. Their contributions involved implementing data import features for DialogFlow and making corresponding changes to existing utility functions. They also made code style improvements, including PEP8 fixes, which ensure code quality and consistency. These changes suggest a focus on enhancing the framework's data handling and integration with external services.
asrspeech-recognitionnatural-language-processingttsspeaker-diarization
purnendu91/scalable_agent
Jul 2018 - Dec 2018
A TensorFlow implementation of Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures.
Contributions:2 PRs, 3 pushes, 2 branches in 4 months
architecturesscalableimportancedeep-learningreinforcement-learning
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