diff --git a/resumes/Murtadha.pdf b/resumes/Murtadha.pdf index da9fc44..1f49483 100644 Binary files a/resumes/Murtadha.pdf and b/resumes/Murtadha.pdf differ diff --git a/resumes/Murtadha.tex b/resumes/Murtadha.tex index 86f5c4f..ed60989 100644 --- a/resumes/Murtadha.tex +++ b/resumes/Murtadha.tex @@ -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}