Nashwan Azhari is a Senior Rust Engineer with 12 years of systems and cloud platform experience, currently building next-generation log management SaaS at NXLog from Timișoara, Romania. A systems engineer at heart, he blends deep Linux and Windows internals knowledge with pragmatic platform work—porting containerd/nerdctl to Windows and contributing Azure-focused infrastructure support in Terraform and the Azure SDK for Go. He’s obsessed with tooling and workflow optimization, and has shipped robust Rust agents and libraries for telemetry, benchmarking, and user-event analytics. Nashwan’s open-source contributions span high-profile projects (containerd, Terraform, Azure SDK) where he improves Windows CI/CD, container runtime compatibility, and cloud resource support—work that reflects both low-level systems instincts and pragmatic cloud engineering.
12 years of coding experience
10 years of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Universitatea „Politehnica” din Timișoara
Contributions:4 reviews, 19 commits, 23 PRs in 11 months
Contributions summary:Nashwan primarily contributed to the Windows CI/CD setup scripts, demonstrating a focus on build environment configuration and automation. Their work included standardizing PowerShell commandlet capitalization, generalizing the setup script for various user environments, and updating dependencies like Golang and MinGW versions. They also addressed issues related to Windows-specific testing and log URI generation, ensuring the stability and compatibility of the project on the Windows platform.
CLI and validation tools for Kubelet Container Runtime Interface (CRI) .
Role in this project:
Back-end & QA Engineer
Contributions:1 review, 10 commits, 5 PRs in 8 months
Contributions summary:Nashwan contributed significantly to the `cri-tools` repository by implementing and integrating benchmarking functionality, including container and pod lifecycle operations. They formalized a channel-based mechanism for collecting and managing benchmark results, ensuring the proper association of operations within larger lifecycles. Moreover, the user added comprehensive performance metrics and streamlined the benchmarking of image-related operations. They also updated the test suite to reflect the new Ginkgo v2 release.
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