Karthik Ramasamy
Co-Founder at V2K Ai
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolKarthik Ramasamy is a seasoned machine learning engineer and founder with 11 years of experience building production-grade AI systems and fraud defenses. Based in San Francisco, he co-founded V2K Ai and advises Oscilar on generative AI while previously leading adversarial ML and real-time fraud teams at Oscilar and Uber. At Google Cloud he worked on TensorFlow for Enterprise and contributed to Kubeflow Fairing—improving function serialization and deployment for ML workflows—demonstrating deep experience in ML model deployment. His earlier career at LinkedIn involved large-scale security analytics and real-time monitoring, and at LogBase he built high-performance analytics and IoT solutions, reflecting strong systems and infrastructure chops. Comfortable moving between research, product, and engineering, he combines hands-on coding (prompt engineering on GitHub) with startup leadership. An under-the-radar strength is his track record of turning complex security and deployment problems into scalable, production systems.
11 years of coding experience
14 years of employment as a software developer
MS Computer Science, MS Computer Science at Columbia UniversityBE Electrical and Electronics, BE Electrical and Electronics at PSG College of Technology
Github Skills (11)
continuous-deployment10
kubeflow10
machine-learning10
pytorch10
python10
ml-deployment10
gcp9
xgboost9
docker8
dockers8
tensorflow8
Programming languages (11)
Github contributions (5)
kubeflow/fairing
Jan 2019 - Aug 2019
Python SDK for building, training, and deploying ML models
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 36 commits, 47 PRs in 7 months
Contributions summary:Karthik primarily contributed to the `fairing` project, focusing on enhancing its machine learning model deployment capabilities. This involved modifying the function preprocessor to leverage `cloudpickle` for serializing functions, and making the prediction endpoint deployment work with the function preprocessor. Key contributions include the creation of an example for training and deploying an XGBoost model and adding support for resource specification for pods. These changes aimed to streamline the process of training and deploying ML models within the Kubeflow environment.
deployingml-modelspythondata-sciencemachine-learning
karthikv2k/fairing
Jan 2019 - Aug 2019
An experimental library to fire of TFJob, PyTorchJob etc... from Jupyter
Contributions:2 PRs, 147 pushes, 81 branches in 7 months
jupyterlab-extensionjupyter-notebookjupyterlabjupytertfjob
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