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Nour Abosen

Nour Abosen

Python AI Specialist | LLM Fine-Tuning & RAG Systems Builder

Egypt flagKafr Elsheikh, Egypt
$10.00/hrEntry LevelOther

Key Skills

Software

Other

Top Subject Matter

No subject matter listed

Top Data Types

DocumentDocument
TextText

Top Task Types

Fine Tuning
Question Answering
Text Generation
Text Summarization
Translation Localization

Freelancer Overview

As a Computer Science Engineering graduate with a strong foundation in mathematics and AI, I bring analytical precision and technical depth to AI training and data labeling tasks. My hands-on experience evaluating and annotating AI-generated content — gained through real-world data labeling projects — complements my academic work in NLP, generative AI, and LLM systems. I’m skilled at assessing response quality, coherence, factual accuracy, and tone — critical for training and refining large language models. What sets me apart is my ability to bridge theory and practice: I’ve built and deployed AI systems like InsightMiner (a RAG-based document summarizer) and ChatBotA (a multi-persona Hugging Face–powered chatbot), giving me deep insight into how models behave and what kind of training data improves them. I’ve also published research in EEG-to-text decoding and diabetes time-series forecasting, demonstrating my ability to handle complex, structured, and domain-specific data. Proficient in Python, NLP pipelines, and research methodologies, I’m well-equipped to contribute to high-impact AI training projects — especially in English-language NLP, conversational AI, technical content, and academic domains.

Entry LevelArabicEnglishJapanese

Labeling Experience

LLM Output Evaluation & Prompt-Response Annotation for Conversational AI

OtherTextClassificationText Generation
Evaluated and annotated multiple of AI-generated prompt-response pairs for quality, safety, coherence, and factual grounding — simulating real-world LLM training data refinement tasks. Tasks included ranking multiple model outputs, flagging hallucinations or toxic content, rewriting ambiguous prompts for clarity, and classifying responses by tone (helpful, neutral, harmful).

Evaluated and annotated multiple of AI-generated prompt-response pairs for quality, safety, coherence, and factual grounding — simulating real-world LLM training data refinement tasks. Tasks included ranking multiple model outputs, flagging hallucinations or toxic content, rewriting ambiguous prompts for clarity, and classifying responses by tone (helpful, neutral, harmful).

2024 - 2024

Education

E

Egypt-Japan University for Science and Technology

Bachelor of Science, Computer Engineering

Bachelor of Science
2020 - 2025
K

Kafr Elsheikh STEM School

Thanaweya A'ma, General Certificate of Education

Thanaweya A'ma
2017 - 2020

Work History

C

CivilSoft

Mobile Development Intern

Kafr Elsheikh
2024 - 2024