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Software Engineer with less than a year in Machine Learning, Data Science, and Software Development.
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Mohamed Mohamed Ibrahim is an undergraduate Computer Engineering student with 11 months of professional experience across software engineering, HPC administration, and data science/ML engineering. Proficient in Python, TensorFlow, PyTorch, and various data-related libraries and databases, he has contributed to projects involving RAG systems, NMT, medical image segmentation, and AI model development. He demonstrates strong problem-solving and analytical skills, consistently achieving high accuracy in complex technical challenges.
Faulty of Engineering, Alexandria University
Bachelor of Computer Engineering · Computer Engineering
August 1, 2021 – June 30, 2026
STEM School
High School Graduate
June 1, 2018 – May 31, 2021
Webalo MENA
Software Engineer
July 1, 2025 – September 1, 2025
India
Applied Innovation Center
HPC Administrator
September 1, 2024 – September 1, 2024
India
SHAI For AI Company
Data Science and ML Engineer
February 1, 2024 – August 1, 2024
India
BRISC2025 Medical Segementation
January 1, 2026 – Present
Enhanced TransNet architecture with spatial attention, MoE and Combo-loss which has got 81.6 mIOU on BRISC2025.
Egyptian Voice Cloning
January 1, 2026 – March 1, 2026
Fine tuned EGTTS to match specific speaker's tone using his/her YouTube videos which was transcibed using Whisper and MasriSwitch models based on LJ dataset format.
French-to-English NMT
December 1, 2025 – December 1, 2025
Implemented in a team of 3 a French-to-English NMT systems using custom Transformer and Bi-LSTM architectures, achieving ~52 BLEU-4 scores.
Multilingual Study agent
November 1, 2025 – Present
Implemented in a team of 2 a Multilingual RAG system to facilitate student learning, utilizing LangChain, HuggingFace, Boto3, SQLAlchemy Postgres, Gemini API, ChromaDB, and MistralOCR.
Named Entity Recognition
November 1, 2025 – November 1, 2025
Implemented in a team of 3 a Word2Vec (SGNS) model from scratch on the CoNLL-2003 dataset to generate custom word embeddings. Fine-tuned a Feed-Forward Neural Network on custom embeddings, achieving a weighted F1-score of 0.93, while achieving weighted F1-score of 0.87. with HMM.
Gloss-Free Sign Language Translation System
October 1, 2025 – Present
Developed an end-to-end translation pipeline adapting the SpaMo architecture with LoRA-tuned FLAN-T5 LLMs, integrating MediaPipe pose features, Sign Contrastive Learning, and Data Augmentation to enhance performance on PHOENIX14T.
Waste Classification System
October 1, 2025 – October 1, 2025
Developed an automated Waste Classification System using TensorFlow and VGG16 to distinguish organic vs. recyclable waste with 78% accuracy.
Environmental Sound Classification
June 1, 2025 – June 1, 2025
Developed deep learning models LSTM, BILSTM, CNN-LSTM, and Transformer-from-scratch on the UrbanSound8K dataset, achieving 70.1% accuracy and 0.718 F1 score with an Adaboosted Transformer ensemble.
Agricultural Land Classification
March 1, 2025 – February 1, 2026
Developed a hybrid CNN-Vision Transformer (ViT) using Keras & PyTorch to classify satellite images with 95% accuracy.
PDF Q&A Bot
March 1, 2025 – February 1, 2026
Architected a Retrieval-Augmented Generation (RAG) application using LangChain and IBM Watsonx.ai, enabling Q&A over PDF documents with Granite LLMs using Chroma DB for vector storage and a Gradio interface for UI.
IBM AI Engineering
IBM
June 1, 2026 – Present
Foundation of GenAI
Udacity
June 1, 2026 – Present
Machine Learning, Could Foundiation
AWS
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects are diverse, covering medical segmentation, multilingual agents, sign language translation, voice cloning, and environmental sound classification, indicating a broad interest in applying AI to various problems. The professional experiences, though short-term, show exposure to software engineering, HPC administration, and data science/ML engineering, suggesting a versatile and adaptable individual. The target role of 'Software Engineer' aligns well with the practical application of their academic and professional skills, particularly in areas requiring ML integration or MLOps.
Soft Skills & Operational Fit
The candidate's project descriptions highlight problem-solving, analytical skills, and teamwork. Participation in Kaggle competitions and developing systems from scratch indicate initiative and critical thinking. The brief professional experiences suggest adaptability and a willingness to learn new technologies (e.g., K8S, Git, HPC systems).