Mohamed S.

Key Skills

Software

Don't disclose

Top Subject Matter

Neuro-Symbolic AI / intelligent systems research

LLM agents / RAG

Top Data Types

Text

Computer Code Programming

Top Task Types

Fine-tuning

Prompt + Response Writing (SFT)

Freelancer Overview

Undergraduate Researcher at Damanhour University (Neuro-Symbolic AI). Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose. Education includes Bachelor of Science, Damanhour University (2025) and Certificate, Information Technology Institute (ITI) (2026). AI-training focus includes data types such as Text and labeling workflows including Fine-tuning and Prompt + Response Writing (SFT).

Labeling Experience

Undergraduate Researcher at Damanhour University (Neuro-Symbolic AI)

Don't discloseTextFine-tuning

As an Undergraduate Researcher focusing on Neuro-Symbolic AI, the role involves working with AI-driven intelligent systems where training data preparation is typically required for model development. The work supports conceptualization and development of intelligent systems using neuro-symbolic approaches. Any model training activities would rely on textual/semantic data representations used during research experimentation. • Collaborated on academic AI research projects • Contributed to intelligent system design • Worked on neuro-symbolic AI concepts for practical problem solving • Supported AI experimentation workflows

2026 - Present

Certificate: Building RAG Agents with LLMs (ITI, 60 Hours)

Don't discloseTextPrompt + Response Writing (SFT)

Completed a 60-hour training focused on building Retrieval-Augmented Generation (RAG) agents with Large Language Models. The course likely includes creating prompts, structuring input-output behavior, and preparing LLM-compatible datasets for agent functionality. Training supports practical application of AI agents where textual data is central to model prompting and response generation. • Trained on LLM-based agent construction • Worked with RAG concepts and workflows • Practiced prompt and response formulation for agent behavior • Completed intensive module content during the 60-hour course

2026 - 2026

Education

Work History