AI Engineer LLM Specialist Open to Work

// Hello, World! I'm

Ahmed Ashraf

AI & Machine Learning Engineer

specializing in |
30+ AI Projects
2 IEEE Papers
3.76 GPA / 4.0
Expert Kaggle Rank

Shebin Al-Qanater, Al-Qalyubia, Egypt

+20 1097436928

Ahmed Ashraf
AI Engineer

01. About Me

I am an AI & Machine Learning Engineer with a strong foundation in AI and Computer Science. I specialize in designing, developing, and optimizing ML and DL models to tackle complex problems in NLP, Computer Vision, and predictive analytics.

Proficient in transformer-based architectures (LLMs) and experienced in MLOps for building scalable, production-ready AI systems. I am passionate about innovation, data-driven decision-making, and delivering impactful, business-focused AI solutions.

Education

Bachelor of Computer Science and Artificial Intelligence

Benha University (Graduated 2024)

GPA: 3.76 / 4.0

AI / ML Training Program

Information Technology Institute (ITI), Menoufia, Egypt (Aug 2023 – Oct 2023)

02. Technical Skills

Languages

Python C++ Java

ML & DL

TensorFlow Keras PyTorch Scikit-learn

NLP & LLMs

NLTK SpaCy Transformers (Hugging Face) LangChain LlamaIndex

Computer Vision

OpenCV Ultralytics YOLO

Data & MLOps

Pandas NumPy SciPy SQL MLflow CML Streamlit Flask/FastAPI Power BI

Soft Skills

Communication Problem Solving Teamwork Time Management

03. Experience

Aug 2023 – Dec 2023

AI & ML Engineer Intern

Electropi (Cairo, Egypt)

  • Conducted sentiment analysis on Amazon reviews using LSTM models to extract customer insights.
  • Enhanced pneumonia diagnosis accuracy in chest X-ray images by applying CNNs.
  • Developed neural network models for market segmentation to improve customer targeting.
  • Built unsupervised models (KMeans, Agglomerative Clustering) for personalized marketing strategies.
  • Classified disaster-related tweets using NLP techniques (Word2Vec, GloVe, TF-IDF).
  • Automated machine learning workflows using PyCaret, improving deployment efficiency.
  • Performed exploratory data analysis (EDA) on Airbnb listings to extract actionable insights.
Aug 2023 – Oct 2023

Data Science Intern

TechnoHacks EduTech (Nashik, India)

  • Developed ML models for email spam detection using Scikit-learn and NLP techniques (TF-IDF, CountVectorizer).
  • Analyzed sentiment in social media posts using NLTK and spaCy to extract audience insights.
  • Built predictive models to forecast employee turnover and optimize retention strategies.

04. Featured Projects

Machine Learning

Machine Learning

Heart Disease Prediction

Ensemble ML models with advanced preprocessing pipelines to improve prediction accuracy for heart disease risk assessment.

  • Ensemble ML
  • Preprocessing
  • Healthcare
Machine Learning

Airline Passenger Satisfaction

Predicted passenger satisfaction using classification with feature engineering, visualized with Power BI dashboards.

  • Classification
  • Feature Eng
  • Power BI
Machine Learning

Liver Disease Prediction

ML models to predict liver disease from patient records, enabling early and accurate clinical detection.

  • ML
  • Healthcare
  • Prediction
Machine Learning

Prostate Cancer Risk

Classifier for prostate cancer risk from lifestyle and medical indicators with Power BI visualizations for clinical insight.

  • Classifier
  • Power BI
  • Medical

Deep Learning

Deep Learning

Brain Tumor Detection

CNN to classify brain tumors from MRI scans with high accuracy, aiding early and reliable medical diagnosis.

  • CNN
  • MRI
  • Medical Imaging
Deep Learning

Pneumonia Classification

Deep learning model classifying pneumonia from chest X-Ray images with high diagnostic accuracy using transfer learning.

  • Deep Learning
  • X-Ray
  • Classification
Deep Learning

Speech Recognition System

DL-based speech-to-text pipeline with robust noise handling and optimized real-time inference performance.

  • DL
  • Audio Processing
  • Real-time
Deep Learning

Elpv Classification

Quality evaluation of solar cells from electroluminescence images using deep learning image analysis.

  • Image Analysis
  • Quality Control
  • DL

Computer Vision

Computer Vision

Bone Fracture Classification

Built a MobileViT Transformer model to classify bone fractures from X-ray images, improving diagnostic efficiency.

  • MobileViT
  • Transformer
  • X-Ray
Computer Vision

ASL Detection

Used CycleGAN to generate synthetic ASL data and trained a model for real-time sign recognition.

  • CycleGAN
  • Real-time
  • Sign Language
Computer Vision

CT Heart Segmentation

Implemented U-Net architecture for precise segmentation of heart structures in CT scans.

  • U-Net
  • Segmentation
  • Medical Imaging
Computer Vision

Retina Vessel Segmentation

Automatic segmentation of blood vessels in retinal images using U-Net for opthalmologic diagnosis.

  • U-Net
  • Retina Analysis
  • Segmentation

Natural Language Processing

NLP

Language Detection

Built an NLP classifier to identify the language of given text samples with high precision.

  • NLP
  • Text Classification
  • Identification
NLP

Arabic Sentiment Analysis

Fine-tuned BERT and trained LSTM to analyze sentiment in Arabic reviews.

  • BERT
  • LSTM
  • Arabic NLP
NLP

Paraphrase Classification

Used XLNet for paraphrase detection and T5 for generating paraphrases with varied structure.

  • XLNet
  • T5
  • Generation
NLP

Longformer Classification

Fine-tuned Longformer model for text classification tasks involving long documents.

  • Longformer
  • Fine-tuning
  • NLP
NLP

Amazon Reviews Analysis

Deep learning based sentimental analysis on large-scale Amazon dataset.

  • Deep Learning
  • Sentiment
  • Big Data

AutoEncoders

AutoEncoder

Cifar10 Image Colorization

AutoEncoder-based model to colorize grayscale images from the Cifar10 dataset.

  • AutoEncoder
  • Image Processing
  • Colorization
AutoEncoder

Visualize Mnist

Dimensionality reduction and visualization of the MNIST dataset using AutoEncoders.

  • AutoEncoder
  • Visualization
  • MNIST

Generative Adversarial Networks (GANs)

GAN

Cycle GAN For ASL

Generating synthetic American Sign Language gestures using CycleGAN for data augmentation.

  • CycleGAN
  • Data Augmentation
  • ASL
GAN

DCGAN For Fashion Minst

Generating realistic fashion items using Deep Convolutional GANs trained on Fashion MNIST.

  • DCGAN
  • Image Generation
  • Fashion MNIST

Generative AI

Generative AI

LLAMA 3.1 RAG Doc QA

Document Question-Answering system using Llama 3.1, Retrieval-Augmented Generation (RAG), and ChromaDB vector store.

  • Llama 3.1
  • RAG
  • ChromaDB
Generative AI

Multi-Doc RAG with Streamlit

Interactive interface for querying multiple documents using RAG and Llama 3.1.

  • RAG
  • Streamlit
  • Llama 3.1
Generative AI

Document Summarizer

Automated document summarization tool powered by LlamaIndex and Llama 3.

  • LlamaIndex
  • Llama 3
  • Summarization

PowerBI Analysis

PowerBI

Airline Passenger Report

Comprehensive Power BI dashboard analyzing passenger satisfaction metrics.

  • Power BI
  • Data Viz
  • Analytics
PowerBI

Healthcare Dashboard

Interactive dashboard for healthcare providers to track key performance indicators.

  • Power BI
  • Healthcare
  • Dashboard
PowerBI

AI/ML Salary Analysis

Visual analysis of salary trends in AI, ML, and Data Science across different regions.

  • Power BI
  • Salary Data
  • Trends

Graduation Project

Graduation Project

Alzheimer’s Detection with GenerativeAI

Innovative solution using AI and Generative technologies for early detection of Alzheimer's symptoms, improving treatment opportunities.

  • GenerativeAI
  • Deep Learning
  • Healthcare
Graduation Project

Classroom Monitor System (EMA)

Real-time AI monitoring for student attendance and behavior using object detection, body tracking, and face recognition.

  • Computer Vision
  • Real-time
  • Tracking

05. Achievements & Publications

Kaggle Expert

Recognized for active participation in competitions, high-quality notebooks, and contributions to the data science community.

Publications

  • Explainable ML for Liver Disease Detection (IEEE, Oct 2024)
    Integrated SHAP, LIME, and Anchors to improve interpretability in diagnosing FLD and HBV.
  • ML-Based Anomaly Detection in Healthcare (IEEE, Oct 2024)
    Survey of ML techniques (e.g., autoencoders, XGBoost) for detecting anomalies in healthcare IoT and WSNs.

Get In Touch

Let's Work Together

I'm currently looking for new opportunities in AI, Machine Learning, and Data Science. Whether you have a specific project or just want to say hi, my inbox is always open!

Shebin Al-Qanater, Al-Qalyubia, Egypt
+20 1097436928

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