Vinay Damodaran
Machine Learning Technical Leader at Cisco
Seattle, Washington, United States
Summary
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Rockstar🎓
Top SchoolVinay Damodaran is a Machine Learning Technical Leader with a decade of experience building and deploying production-grade ML systems, currently leading Collab AI initiatives at Cisco in Seattle. He combines research-grade NLP expertise from Carnegie Mellon’s Language Technologies Institute with hands-on engineering from roles at Oracle and Cisco, bridging model development and scalable application delivery. His contributions to the open-source MindMeld conversational AI platform reflect a focus on backend robustness and real-world deployment issues like Docker workflows, caching, and safe-path handling. Known for turning prototypical ML models into reliable services, he brings both academic rigor and product-oriented pragmatism to cross-functional teams.
10 years of coding experience
7 years of employment as a software developer
Master of Science - MS Intelligent Information Systems, Master of Science - MS Intelligent Information Systems at Carnegie Mellon UniversityBachelor of Technology - BTech Computer Science, Bachelor of Technology - BTech Computer Science at National Institute of Technology Calicut
English, Malayalam, Hindi
Stackoverflow
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Github Skills (7)
back-end-development10
python10
dockers9
docker9
api8
apidoc8
documentation7
Programming languages (9)
Github contributions (5)
cisco/mindmeld
Jun 2020 - Nov 2022
An Open Source Conversational AI Platform for Deep-Domain Voice Interfaces and Chatbots.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:4 releases, 79 reviews, 114 commits in 2 years 4 months
Contributions summary:Vinay primarily contributed to the MindMeld platform's back-end functionality. They focused on resolving docker-related documentation issues and made code modifications to alter the frame type within the application manager. Furthermore, they addressed a bug related to safe paths and updated the query cache in response to TPP updates. The changes suggest a focus on core system components and improving the platform's performance.
conversational-ai
vrdn-23/vllm
May 2024 - Jul 2026
A high-throughput and memory-efficient inference and serving engine for LLMs
Contributions:145 pushes, 12 branches in 2 years 2 months
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