Rajib Hossen is a Senior Software Engineer and Cloud Performance & Capacity specialist with 11 years of experience optimizing large-scale distributed systems, Kubernetes platforms, and HPC workflows. He holds a Ph.D. in Computer Science from UT Arlington where his research produced novel autoscaling and resource-management algorithms that cut microservice CPU usage by over 33% compared to prior approaches. At GEICO he leads data-driven right-sizing, cost attribution, and cloud efficiency efforts, translating workload signals into measurable cost and performance improvements. His internships at Lawrence Livermore National Laboratory involved enabling autoscaling for HPC on Kubernetes and building Terraform/AWS automation for converged computing—a practical bridge between cloud-native tooling and HPC needs. Rajib combines hands-on systems coding in Go and Python, observability stacks (Grafana/InfluxDB), and research-grade experimentation to solve hard capacity and sustainability problems. He’s particularly interested in making large compute environments both performant and cost-efficient, with an eye toward practical deployment of research innovations.
11 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at The University of Texas at Arlington
Bachelor of Science (B.Sc.), Computer Science and Engineering, Bachelor of Science (B.Sc.), Computer Science and Engineering at Khulna University of Engineering and Technology
computation offloading in mobile edge computation using Reinforcement Learning
Contributions:75 commits, 2 comments in 11 months
mobilereinforcement-learning
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