Ramraj Chandradevan
Applied Scientist II at Amazon
Atlanta, Georgia, United States
Summary
👤
Senior🎓
Top SchoolRamraj Chandradevan is an Applied Scientist II with eight years of experience at the intersection of NLP, information retrieval, and multimodal learning, recently joining Amazon after completing a PhD at Emory University. His research spans domain adaptation for retrieval, cross-lingual/multilingual search, recommender systems, VQA, computer vision, and graph neural networks, and he has practical industry experience from internships at Microsoft, Home Depot, and Amazon’s Alexa team. Lately he has focused on LLM-driven workflows—synthetic data generation, RAG systems, and fine-tuning—bringing research prototypes toward production-ready solutions. Based in Atlanta, he pairs academic rigor with hands-on engineering across search, ranking, and multimodal pipelines, and his background in electronics and even pilot training hints at a methodical, systems-oriented problem-solving approach.
8 years of coding experience
1 year of employment as a software developer
Bachelor’s Degree, Electronics and Telecommunication Engineering, Bachelor’s Degree, Electronics and Telecommunication Engineering at University of MoratuwaFlying License, Private Pilot License course, Flying License, Private Pilot License course at Skyline AviationDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Emory UniversitySummer Course, EFL, Summer Course, EFL at Wake Technical Community College
English, Tamil
Github Skills (144)
python10
record-linkage10
representations10
nlp10
lucene10
sparse10
retrieval10
information-retrieval10
natural-language-processing10
pytorch10
machine-learning10
similarity10
recommender9
transformer9
sentence9
Programming languages (6)
Github contributions (5)
cramraj8/AI-BipedalWalker-Algorithms
Dec 2019 - May 2021
Contributions:1 PR, 5 pushes, 1 branch in 1 year 5 months
cramraj8/TFSurvivalRegression
Sep 2017 - Oct 2017
Contributions:36 commits, 5 PRs, 34 pushes in 1 month
reinforcement-learningsurvivalsurvival-modelslim
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