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refactor(software): update resume for a co-op engineer at huawei

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Murtadha 2025-05-04 21:06:31 -04:00
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resumes/Murtadha.tex
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@ -120,12 +120,12 @@
%----------Highlights----------
\section{\color{blue}DevOps \& Cloud Highlights}
\resumeItemListStart
\resumeItem {\textbf{\color{black}IaC \& Pipelines:} Terraform modules plus self-hosted GitLab→Jenkins CI for repeatable build→test→deploy across AWS and AKS sandbox \vspace{-5pt}}
\resumeItem {\textbf{\color{black}Containerization:} Dockerised multi-service stacks; private Harbor registry; proof-of-concept AKS autoscaling tests \vspace{-5pt}}
\resumeItem {\textbf{\color{black}Cloud Platforms:} AWS (production) · Azure (learning — AKS, Azure CLI, Terraform provider) \vspace{-5pt}}
\resumeItem {\textbf{\color{black}Security:} HTTPS via Let’s Encrypt auto-renewal with Nginx reverse proxy; .env secret management; exploring container image-scan gates \vspace{-5pt}}
\resumeItem {\textbf{\color{black}IaC \& Pipelines:} Terraform modules plus self-hosted GitLab→Jenkins CI for repeatable build→test→deploy across AWS.\vspace{-5pt}}
\resumeItem {\textbf{\color{black}Containerisation:} Dockerised multi-service stacks; private Harbor registry for image promotion across stages.\vspace{-5pt}}
\resumeItem {Analysed edge→cloud TCP flows with \textbf{Wireshark} and \textbf{iperf3}, guiding socket-buffer tuning for latency studies.\vspace{-5pt}}
\resumeItem {Generated synthetic database traffic with \textbf{iperf3} to stress-test cloud-datacentre paths and tune socket buffers for latency.\vspace{-5pt}}
\resumeItemListEnd
\vspace{-8pt}
\vspace{-5pt}
%----------Experience----------
\section{\color{blue}Relevant Work Experience}
@ -134,11 +134,10 @@
{Software Engineer - Machine Learning}{Jan 2024 -- Dec 2024}
{University of Guelph}{Guelph, Ontario}
\resumeItemListStart
\resumeItem {Drove a \textbf{30$\times$ bandwidth reduction} and \textbf{29\% latency cut} by integrating Swin-Transformer semantic compression into an edge–cloud pipeline, while keeping image fidelity $\ge$88\%}
\resumeItem {Drove a \textbf{30$\times$ bandwidth reduction} and \textbf{29\% latency cut} by integrating Swin-Transformer semantic compression into an edge-cloud pipeline, while keeping image fidelity $\ge$88\%}
\resumeItem {Authored an \textbf{adaptive network-aware module} that tunes compression in real time, ensuring lowest-possible latency under fluctuating link conditions}
\resumeItem {Quantised the PyTorch model to ONNX and \textbf{deployed on Xilinx Kria SoCs with Vitis AI}, achieving DPU inference $\sim$3$\times$ faster than CPU baseline}
\resumeItem {Containerized encoder, decoder, and a core-network simulator; spun up reproducible test-beds via \textbf{Gogs\,$\rightarrow$\,Jenkins CI} and Terraform for automated build $\rightarrow$ test $\rightarrow$ deploy cycles}
% \resumeItem {Published findings at \textbf{IEEE CCECE 2024}; additional journal article in preparation.}
\resumeItem {Containerised encoder, decoder, and a 10 GbE core-network simulator (TCP/IP); profiled round-trip times with iperf3 to verify 29\% latency optimizations}
\resumeItemListEnd
\resumeSubheading
@ -169,7 +168,7 @@
\textbf{\color{black}Languages:} & C/C++, Python, Rust, Java, SQL, Bash, JavaScript, HTML, CSS, CMake \\
\textbf{\color{black}Frameworks:} & PyTorch, TensorFlow, Node.js, React, Express.js, ROS \\
\textbf{\color{black}Cloud and DevOps:} & AWS, Azure, Docker, Kubernetes, Terraform, Jenkins, PostgreSQL, MongoDB, SQLite \\
\textbf{\color{black}Tools and Protocols:} & Git, GitHub, Postman, Flask, Swagger, Jira, HTTP, TCP \\
\textbf{\color{black}Tools and Protocols:} & Git, GitHub, Postman, Flask, Swagger, Jira, HTTP, TCP/IP, UDP, MQTT, iperf3 \\
\end{tabular}
\vspace{-8pt}