Nirav Patel is a software engineer with 11 years of experience who blends production-grade systems work in networking with applied machine learning for climate and weather forecasting. He currently contributes to Google Compute Engine and previously spent years improving protocol scalability and multicast features across Juniper’s JunOS and EVO platforms. As a master’s student in Computer Science at NC State (4.0 GPA), he has built cloud-native data pipelines and graph and DynamoDB schemas for large climate datasets, including automated AWS stacks and GraphQL APIs to make GHCN-D more accessible. His research work applies deep learning to South Pacific weather prediction, reflecting a practical interest in turning ML models into usable services. Comfortable across systems, cloud, and ML stacks, he combines low-level debugging and performance tuning with data engineering and model development.
11 years of coding experience
10 years of employment as a software developer
Electrical and Computer Engineering, Electrical and Computer Engineering at Carnegie Mellon University
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