Ramanan Balakrishnan
Senior Software Engineer at Google
Singapore, Singapore
Summary
👤
Senior🎓
Top SchoolRamanan Balakrishnan is a Senior Software Engineer with 11 years of experience building large-scale ML and real-time systems, currently protecting over 100M daily transactions as part of Google’s payments risk team for GPay India. He specializes in deploying AI models for fraud prevention at scale and brings a strong background in data science from his early role at Semantics3, where he led taxonomy classification, information extraction and real-time data processing initiatives. His engineering work spans production-grade NLP contributions to the popular spaCy project—improving Doc/Span analytics and dependency parsing utilities—alongside deep expertise in signal-level research from a prior career in electromagnetics and radar. Comfortable moving between research and product delivery, he architects data wrangling and ML pipelines while emphasizing engineering best practices. Based in Singapore, he combines rigorous academic training in electrical engineering with hands-on experience shipping robust, high-throughput systems.
11 years of coding experience
8 years of employment as a software developer
Master of Engineering (M.Eng.), Electrical Engineering, Master of Engineering (M.Eng.), Electrical Engineering at National University of Singapore
Github Skills (12)
nlp10
python10
natural-language-processing10
cython10
numpy10
machine-learning9
artificial-intelligence8
data-science8
tokenize7
tokenizer7
api6
api-design6
Programming languages (6)
Github contributions (5)
explosion/spaCy
Oct 2017 - Jun 2019
💫 Industrial-strength Natural Language Processing (NLP) in Python
Role in this project:
Back-end Developer
Back-end DeveloperContributions:10 commits, 5 PRs, 4 comments in 1 year 7 months
Contributions summary:Ramanan primarily contributed to the `spacy` library by modifying core functionalities related to the `Doc` and `Span` objects. Their commits focused on enhancing the `to_array` method to support a single attribute, strings, and more robust handling of attribute lists. Additionally, the user introduced the `get_lca_matrix` method for both `Span` and `Doc` objects, demonstrating a focus on improving the library's analytical capabilities, specifically for dependency parsing. The user also corrected the BILUO annotation format.
fairness-mlpythondata-preprocessinglanguage-processingtokenization
ramananbalakrishnan/nus-thesis-latex-template
Jan 2015 - Nov 2015
Contributions:17 commits, 9 pushes, 1 branch in 9 months
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