Pradeep Thallapally
Software Engineer III at Google
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolPradeep Thallapally is a Software Engineer III at Google with a decade of experience building machine learning and large-scale data systems. He combines hands-on ML model development in TensorFlow/Keras with production engineering skills honed across roles at Google, Microsoft, and Morgan Stanley. His background includes real-time fraud detection pipelines using Spark, Kafka, HBase, and end-to-end ML deployments, reflecting a strong focus on operability and scalable data infrastructure. An active open-source contributor, he has implemented core deep learning assignments—CNNs, RNNs/LSTMs, and residual networks—demonstrating practical expertise in image and sequence modeling. Based in Mountain View, he brings both research internship experience from IIT Bombay and a top-ranked CS degree from NIT Karnataka, blending academic rigor with product-focused delivery. Notably, he has a track record of turning prototype models into reliable pipelines that serve production needs.
10 years of coding experience
3 years of employment as a software developer
Bachelor’s Degree, Computer Science, 9.18, Bachelor’s Degree, Computer Science, 9.18 at National Institute of Technology Karnataka
English, Telugu, Hindi
Github Skills (9)
neural-network10
keras10
lstm10
recurrent-neural-networks10
convolutional-neural-networks10
deep-learning10
tensorflow10
python10
image-classification9
Programming languages (3)
Github contributions (5)
Gurupradeep/deeplearning.ai-Assignments
Nov 2017 - Nov 2020
Role in this project:
Data Scientist
Data ScientistContributions:47 commits, 1 PR, 44 pushes in 3 years
Contributions summary:Pradeep added assignments related to deep learning, including the implementation of basic neural network functions, building convolutional neural networks, creating residual networks, and exploring recurrent neural networks with LSTMs. The contributions demonstrate proficiency in implementing fundamental deep learning concepts, specifically within the context of image classification and sequence models. The commits also show an understanding of training and applying models within the Keras and TensorFlow frameworks.
convolutional-neural-networkslstmneural-networksrnnsequence-to-sequence
Gurupradeep/FCN-for-Semantic-Segmentation
Sep 2017 - Nov 2017
Implemention of FCN-8 and FCN-16 with Keras and uses CRF as post processing
Contributions:51 commits, 49 pushes, 1 branch in 1 month
post-processingfcndeep-learningcrftensorflow
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