Salma Mohammed hamed
@salmamohammedhamed
AI engineer specializing in ML, deep learning, and LLM systems.
What I'm looking for
I am an AI engineer focused on turning research-driven methods into scalable, real-world AI solutions across computer vision, NLP, speech, and multimodal applications. I have hands-on experience in model optimization, adversarial robustness, LLM fine-tuning, RAG, and deploying models with Docker and production pipelines.
My background includes research projects that improved robustness (e.g., U-Net Dice from 0.36 to 0.94 and major reductions in NLP/ASR attack success), internships delivering ML integration and QA automation, and building end-to-end systems such as multimodal video summarizers, domain-specific LLMs for Verilog generation, and soccer video analytics.
Experience
Work history, roles, and key accomplishments
AI Engineer Intern
Al-Arcade
Aug 2025 - Present (1 month)
Researching and developing AI agent architectures and RAG-based QA systems for gamified solutions, and integrating ML models into production pipelines to deliver client-facing features.
Researcher — Graduation Project
Si-Vision
Oct 2024 - Jul 2025 (9 months)
Conducted adversarial ML research across vision, NLP, LLMs, and ASR, implementing attacks and defenses and improving model robustness (e.g., U-Net Dice from 0.36 to 0.94; NLP attack success reduced 76.91%).
QA Engineer Intern
Siemens
Oct 2024 - Dec 2024 (2 months)
Researched 5G PRACH and EVM, developed MATLAB scripts for PRACH generation and EVM validation, and automated testing workflows with Bash and Python to improve validation efficiency.
Education
Degrees, certifications, and relevant coursework
Cairo University
Bachelor of Engineering, Electronics and Communications Engineering
2020 - 2025
Grade: 81% (Very Good)
Activities and societies: Graduation project on adversarial attacks and robustness in deep learning; participated in Siemens 5G internship and related diplomas.
Pursuing a Bachelor in Electronics and Communications Engineering with coursework and projects focused on AI, embedded systems, and communications, completed in 2025 with a GPA of 81% (Very Good).
Tech stack
Software and tools used professionally
Availability
Location
Authorized to work in
Job categories
Skills
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