Rakesh Chada
Senior Applied Scientist at Amazon
United States
Summary
👤
Senior🎓
Top SchoolRakesh Chada is a Senior Applied Scientist with 11 years of experience building large-scale, multimodal foundation models and production ML systems at Amazon, after shaping AI assistants at x.ai. He combines deep research skills—developing question answering, error detection, and natively multimodal models—with practical engineering chops from roles at Oracle and SAP where he built scalable, performance-tuned systems. Comfortable spanning the research-to-production gap, he has hands-on experience in NLP, temporal modeling, and large-data graph analytics from his Stony Brook research, and has contributed QA improvements to the popular keras-preprocessing library. Based in the United States, he brings a track record of shipping robust ML solutions in both startup and enterprise environments and a knack for verifying model and data-processing correctness that often goes unnoticed.
11 years of coding experience
7 years of employment as a software developer
BITS Pilani, Birla Institute of Technology and ScienceArtifical Intelligence, Artifical Intelligence at U C Berkeley - Online courseSaaS, SaaS at U C Berkeley - Online CourseMaster's Degree Computer Science, Master's Degree Computer Science at Stony Brook University
English, Telugu, Hindi
Github Skills (6)
pytest10
python10
test-automation10
testing10
natural-language-processing9
nlp9
Programming languages (3)
Github contributions (5)
keras-team/keras-preprocessing
Jun 2018 - Jun 2018
Utilities for working with image data, text data, and sequence data.
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:5 commits, 1 PR, 1 comment in 1 day
Contributions summary:Rakesh focused on improving the testing coverage and quality of the `keras-preprocessing` library. Their contributions included adding tests for the `lower` flag within the `Tokenizer` class, ensuring proper behavior with both word and character-level tokenization. They also addressed code style issues by removing redundant backslashes and adding blank lines to improve code readability within the test suite. Their work demonstrates a focus on verifying the correct functionality of text processing features.
text-datasequencepythonimage-data
rakeshchada/learning-deep-learning
Jan 2017 - Jun 2019
Contributions:22 commits, 19 pushes, 1 branch in 2 years 5 months
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