Prasanna Patil
Software Engineer at Google DeepMind
Bengaluru, Karnataka, India
Summary
🤩
Rockstar🎓
Top SchoolPrasanna Patil is a software engineer with 11 years of experience, specializing in applied AI/ML and productionizing large language models to improve NER, relevance, and recommendation systems. He has shipped ML-driven features at scale across Google and now Google DeepMind, and previously developed a neural ranking personalization approach during an Amazon internship that delivered measurable offline gains. A strong academic foundation from IISc (MTech) complements hands-on contributions to notable open-source projects—adding a PReLU activation to the mlpack C++ library and front-end ML integrations for the Oppia learning platform. Prasanna blends research-informed model development with pragmatic engineering for production systems, and he’s known for continuously acquiring new skills and bridging frontend, backend, and ML stack gaps.
11 years of coding experience
5 years of employment as a software developer
Master of Technology - MTech, Computer Science, 9.6/10, Master of Technology - MTech, Computer Science, 9.6/10 at Indian Institute of Science (IISc)Bachelor of Engineering - BE, Computer Engineering, 8.97/10, Bachelor of Engineering - BE, Computer Engineering, 8.97/10 at L.D. College of Engineering
Hindi, Gujarati, Marathi, English
Github Skills (17)
javascript10
c-language10
machine-learning-models10
css10
machine-learning10
deep-learning10
front-end-development10
cplus10
html10
code-editor10
relu10
cpp10
cprogramming-language10
typescript9
typescript-types9
Programming languages (4)
Github contributions (5)
oppia/oppia
Jan 2016 - May 2021
A free, online learning platform to make quality education accessible for all.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:73 reviews, 96 commits, 123 PRs in 5 years 4 months
Contributions summary:Prasanna primarily contributed to the frontend aspects of the project by modifying HTML, JavaScript, and CSS files related to the CodeRepl interaction. They fixed bugs, added features such as the implementation of confidence estimations and added more features for the training interface. They also worked on incorporating these features and components within the exploration player. Furthermore, the user worked to integrate changes for a machine learning based solution.
appenginereactpythonlearning-platformquality-education
mlpack/mlpack
Feb 2017 - Feb 2017
mlpack: a fast, header-only C++ machine learning library
Role in this project:ML Engineer
ML EngineerContributions:8 commits, 2 PRs, 26 comments in 3 days
Contributions summary:Prasanna contributed to the implementation of the PReLU (Parametric Rectified Linear Unit) activation function within the `mlpack` machine learning library. This involved defining the PReLU layer, including its forward and backward passes, and the gradient calculation. Furthermore, the user added tests to verify the activation, derivative, and gradient calculations for the PReLU function. These changes focused on extending the library's capabilities for deep learning tasks.
regressionheaderdeep-learningscientific-computingc-plus-plus
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