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El-Hussein Salah

El-Hussein Salah

AI Engineer

Egypt flagRemote, Egypt
$10.00/hrEntry LevelOther

Key Skills

Software

Other

Top Subject Matter

Ai/nlp Domain Expertise
Retrieval-Augmented Generation Systems
Nlp Domain Expertise

Top Data Types

TextText
DocumentDocument
ImageImage

Top Task Types

Text Generation
Classification

Freelancer Overview

AI Engineer Training (RAG system and LLM dynamic routing). Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Bachelor of Science, Benha University (2022). AI-training focus includes data types such as Text, Document, and Image and labeling workflows including Text Generation and Classification.

Entry LevelEnglishArabic

Labeling Experience

Plant Disease Classification Web App project

OtherImageClassification
Built deep learning models for plant disease classification from leaf images via CNN architectures and curated, labeled image datasets. Labeled images for training, validation, and testing to support model accuracy. Annotated disease presence across multiple plant species using consistent criteria. • Labeled plant leaf images according to visible disease types. • Divided annotated dataset into train/validation/test splits. • Contributed to image preprocessing and augmentation. • Evaluated CNN output to identify misclassified samples and enhance dataset.

Built deep learning models for plant disease classification from leaf images via CNN architectures and curated, labeled image datasets. Labeled images for training, validation, and testing to support model accuracy. Annotated disease presence across multiple plant species using consistent criteria. • Labeled plant leaf images according to visible disease types. • Divided annotated dataset into train/validation/test splits. • Contributed to image preprocessing and augmentation. • Evaluated CNN output to identify misclassified samples and enhance dataset.

2025 - 2025

HCIA AI Training (Customer Churn Analysis project)

OtherDocumentClassification
Participated in data preprocessing, feature engineering, and training for customer churn analysis using machine learning and deep learning models. Labeled customer data by assigning class labels based on churn likelihood. Involved in annotation, verification, and model evaluation steps for supervised model development. • Annotated and labeled structured customer datasets for churn prediction. • Engaged in data cleaning and verification prior to model training. • Analyzed prediction outputs for further training set enhancements. • Collaborated with the team to maintain consistent annotation standards.

Participated in data preprocessing, feature engineering, and training for customer churn analysis using machine learning and deep learning models. Labeled customer data by assigning class labels based on churn likelihood. Involved in annotation, verification, and model evaluation steps for supervised model development. • Annotated and labeled structured customer datasets for churn prediction. • Engaged in data cleaning and verification prior to model training. • Analyzed prediction outputs for further training set enhancements. • Collaborated with the team to maintain consistent annotation standards.

2025 - 2025

NLP Training (ITIDA)

OtherTextText Generation
Contributed to Natural Language Processing (NLP) applications focused on data extraction and semantic search in RAG systems. Worked with real-world datasets to enhance LLM responses and improve data retrieval effectiveness. Participated in data curation, annotation, and prompt engineering tasks for AI training. • Assisted in labeling and annotating large text corpora for NLP projects. • Developed and validated prompt-response pairs for fine-tuning LLMs. • Evaluated accuracy and relevancy of AI-generated answers against ground truths. • Analyzed datasets to enhance NLP training pipelines.

Contributed to Natural Language Processing (NLP) applications focused on data extraction and semantic search in RAG systems. Worked with real-world datasets to enhance LLM responses and improve data retrieval effectiveness. Participated in data curation, annotation, and prompt engineering tasks for AI training. • Assisted in labeling and annotating large text corpora for NLP projects. • Developed and validated prompt-response pairs for fine-tuning LLMs. • Evaluated accuracy and relevancy of AI-generated answers against ground truths. • Analyzed datasets to enhance NLP training pipelines.

2025 - 2025

AI Engineer Training (RAG system and LLM dynamic routing)

OtherTextText Generation
Developed and trained a Retrieval-Augmented Generation (RAG) system utilizing Milvus and LangChain4j to optimize semantic search and backend question answering. Focused on integrating LLMs and refining text embeddings to provide accurate context and responses. Participated in AI model optimization, architecture improvement, and deployment workflows. • Created text embeddings from unstructured data for semantic retrieval. • Labeled and curated datasets for training and validation. • Integrated prompts and responses for enhanced language model output. • Evaluated system performance and annotated edge cases for model improvement.

Developed and trained a Retrieval-Augmented Generation (RAG) system utilizing Milvus and LangChain4j to optimize semantic search and backend question answering. Focused on integrating LLMs and refining text embeddings to provide accurate context and responses. Participated in AI model optimization, architecture improvement, and deployment workflows. • Created text embeddings from unstructured data for semantic retrieval. • Labeled and curated datasets for training and validation. • Integrated prompts and responses for enhanced language model output. • Evaluated system performance and annotated edge cases for model improvement.

2025 - 2025

Sentiment Analysis (Classification) project

OtherTextText Generation
Conducted sentiment analysis on Arabic text datasets using a variety of neural architectures including RNNs, LSTMs, BERT, and GPT. Labeled sentiment for input samples and designed prompt-response datasets for evaluation. Compared accuracy and efficiency in handling complex linguistic patterns via manual annotation work. • Annotated Arabic text data with sentiment categories for supervised training. • Developed custom prompts for classifying ambiguous sentiment. • Supported data cleaning and annotation verification for reliable evaluation. • Compiled results to inform further training iterations.

Conducted sentiment analysis on Arabic text datasets using a variety of neural architectures including RNNs, LSTMs, BERT, and GPT. Labeled sentiment for input samples and designed prompt-response datasets for evaluation. Compared accuracy and efficiency in handling complex linguistic patterns via manual annotation work. • Annotated Arabic text data with sentiment categories for supervised training. • Developed custom prompts for classifying ambiguous sentiment. • Supported data cleaning and annotation verification for reliable evaluation. • Compiled results to inform further training iterations.

2024 - 2024

Education

B

Benha University

Bachelor of Science, Artificial Intelligence

Bachelor of Science
2022

Work History

O

Orange Digital Center

AI Intern

Cairo
2025 - 2025
N

NTI

AI Trainee

Benha
2025 - 2025