Gautham Pughazhendhi is a software engineer specializing in recommendation systems and feature-store infrastructure with 10 years of experience building ML-driven products. At Electronic Arts he implemented a Kubernetes materialization engine and deployment pipelines that cut feature ingestion from 25 to 5 minutes and helped a friends-recommendation model drive a 35% lift in invite acceptance (an estimated $14.5M annual revenue impact). He combines solid ML model development (scikit-learn, TensorFlow, PyTorch) and data engineering with backend and web skills (Python, Flask/Django, React, SQL/NoSQL) to deliver end-to-end solutions and cost-saving model training platforms. Past work includes enterprise chatbot and contextual AI agents that reduced support costs by 70%, plus academic grounding from a Master of Data Science at UBC. Practical strengths include productionizing models at scale, integrating metadata and multi-tenancy for feature services, and a knack for squeezing deployment and training costs down through automation and tooling.
10 years of coding experience
3 years of employment as a software developer
Master of Data Science, Data Science, 96.1%, Master of Data Science, Data Science, 96.1% at The University of British Columbia
Contributions:2 PRs, 201 pushes, 2 branches in 2 years 1 month
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