Aravind Anantha
Sr Machine Learning Engineer at Motorola Solutions
Somerville, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolAravind Anantha is a senior machine learning engineer with nine years of experience building data-driven systems and production ML at Motorola Solutions, backed by a Master’s in Computer Science from NC State. He blends hands-on ML engineering—TensorFlow, Spark, Scikit-learn—with strong software and networking foundations from roles at Cisco and internships, enabling end-to-end solutions from data pipelines to deployment. Notably, he prototyped a Python-based NLP chat-bot at State Auto that used clustering and sentiment tracking to automate insurance agent support and escalate to humans when needed. His open-source contributions include back-end improvements to the NSF-supported Expertiza peer-review platform, focusing on invitation and team-management logic. Based in Somerville, MA, he brings a pragmatic, systems-oriented approach to ML problems and an uncommon combination of networking (CCNA) and ML tooling expertise.
9 years of coding experience
2 years of employment as a software developer
Master’s Degree, Computer Science, 3.74, Master’s Degree, Computer Science, 3.74 at North Carolina State UniversityBachelor of Engineering (B.E.), Computer Science, Bachelor of Engineering (B.E.), Computer Science at Birla Institute of Technology and Science
Tamil, Telugu, Hindi, English
Github Skills (8)
ruby-rails10
rails10
ruby10
st9
code-review7
peer-to-peer7
reviewboard7
peer7
Programming languages (4)
Github contributions (5)
expertiza/expertiza
Mar 2017 - Apr 2017
Expertiza is a web application through which students can submit and peer-review learning objects (articles, code, web sites, etc). The Expertiza project is supported by the National Science Foundation.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:39 commits, 1 PR, 2 comments in 16 days
Contributions summary:Aravind primarily worked on the `invitation_controller.rb` and related files, suggesting a focus on the application's user invitation and team management functionalities. Their contributions included bug fixes, code refactoring, and improvements to the invitation process. The user made iterative changes to the code, including adding comments, removing nested if-else statements, and reducing the ABC size of functions.
nationalpeer-reviewexpertiza-wikidjangoexpertiza
kira0992/expertiza
Mar 2017 - May 2017
Contributions:54 pushes, 1 branch in 1 month
nationalpeer-reviewnational-science-foundationsciencedjango
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