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\begin{document}
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%----------HEADING-----------------
\begin{tabular*}{\textwidth}{l@{\extracolsep{\fill}}r}
  \textbf{\LARGE Abdallah Abouabdallah}  
  \\*Aachen, Germany
   & \href{mailto:abdallah.abouabdallah@rwth-aachen.de}{abdallah.abouabdallah@rwth-aachen.de}
 \\
  \href{https://github.com/AbdallahAbou/}{Github} | \href{https://www.abdallah.sh}{www.abdallah.sh} & +49 1573 6126877\\
  
  
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\vspace{2pt}
\noindent Software engineer specialized in data engineering, MLOps infrastructure, and distributed systems using Node.js, Python, PostgreSQL, Docker and AWS. Proven experience building high-throughput data pipelines, containerized ML deployments, and scalable microservices. Healthcare domain expertise with focus on GDPR-compliant solutions.

\vspace{2pt}
{\textbf{Technical Skills:} JavaScript/Node.js, Python, PostgreSQL, MongoDB | Kubernetes, RabbitMQ, Docker, Git, Linux | MLOps \& DevOps pipelines}

%-----------EDUCATION-----------------
\section{Education}
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	\jobDescription
	{FH Aachen University of Applied Sciences}{Aachen, Germany}
	{Master of Science in Applied Data Science and Medical Engineering.}{2024 - 2026}
	{}
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	\jobDescription
	{FH Aachen University of Applied Sciences}{Aachen, Germany}
	{Bachelor of Engineering in Medical Engineering with Computer Science Minor}{2020 - 2024}
	{}
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%-----------EXPERIENCE-----------------
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\section{Experience}
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    {Uniklinik RWTH Aachen}{Aachen, Germany}
    {Software Engineer}{2024 - Present}
    {}
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    \begin{itemize}
    \jobAchievement{Developed scalable data pipelines for cleaning and standardizing large healthcare datasets; integrated LLM APIs for initial labeling and fine-tuned ML models to ensure compliance and usability in clinical research.}
    \jobAchievement{Optimized legacy ML workflows into containerized MLOps setups using Docker, enabling reproducible deployments and cross-lab validations.}
    \jobAchievement{Built centralized infrastructure with VPS-PostgreSQL integration; automated data migration, deployed secure collaborative environments and improved team efficiency.}
    \jobAchievement{Built full-stack Node.js/React authentication portal for VPS access; integrated multimodal LLMs for data analysis, supporting collaborative predictive modeling in distributed systems.}
    \end{itemize}
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    \jobDescription
      {Forschungszentrum J\"ulich}{J\"ulich, Germany}
      {Research Software Engineer}{2022 - 2023}
      {}
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      \begin{itemize}
        \jobAchievement{Analyzed 3D cellular images from confocal microscopy, focusing on high-precision segmentation.}
        \jobAchievement{Applied ML algorithms to classify cellular objects across datasets.}
        \jobAchievement{Designed visual reports for interdisciplinary teams, aiding critical decision-making.}
    \end{itemize}
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%-----------TECHNICAL STANDARDS-----------------
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\section{Medical \& Technical Standards}
\begin{itemize}[label={}]
    \resumeSubItem{Medical Imaging}{DICOM, NIfTI, TIFF microscopy formats, HL7 FHIR.}
    \resumeSubItem{Regulatory Frameworks}{EU MDR, IEC 62304 (Medical device software), ISO 13485 (Quality management).}
    \resumeSubItem{Data Protection}{GDPR compliance, pseudonymization/anonymization for clinical data.}
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%-----------ACHIEVEMENTS-----------------
\section{Achievements}
\begin{itemize}[label={}]
    \resumeSubItem{1st Place - Telekom GmbH Hackathon (2025)}{Doc2Chat: Intelligent document interaction system using RAG and LLM agents for natural language querying of complex documents. (\href{https://github.com/SeibelT/doc-chat}{GitHub})}
\end{itemize}

%-----------PROJECTS-----------------
\section{Selected Projects}
\begin{itemize}[label={}]
    \resumeSubItem{Distributed Data Collection System}{Built scalable microservices architecture for automated web data aggregation. Implemented rate limiting, proxy rotation, concurrent request handling with RabbitMQ, and PostgreSQL storage.}
    \resumeSubItem{Cross-Institutional ML Pipeline}{Developed containerized framework for reproducible ML model deployment across clinical research sites. Addresses data heterogeneity and ensures GDPR-compliant external validation with minimal technical overhead for partner institutions.}
    \resumeSubItem{Production LLM Application}{Full-stack document retrieval system with agentic workflows. Backend uses vLLM for inference optimization and RAG for context-aware responses. Containerized architecture enables deployment across diverse clinical IT environments.}
    \resumeSubItem{3D Liver Tumor Segmentation}{Implemented cascaded Res-Attention-UNet architecture for automated tumor delineation in CT imaging. Optimized for clinical workflow integration with DICOM compatibility and volumetric analysis reporting.}
\end{itemize}

%-----------LANGUAGES-----------------
\section{Languages}
\begin{itemize}[label={}]
    \resumeSubItem{Proficiency}{German (C1), English (fluent), French (fluent), Arabic (fluent).}
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