Mohamed E.

Key Skills

Software

Other

Top Subject Matter

Arabic linguistic data processing (audio transcription)

LLM output evaluation for Arabic language quality

Arabic proofreading for dataset quality

Top Data Types

Audio

Text

Document

Top Task Types

Transcription

Freelancer Overview

Independent AI Trainer & Data Annotator (Self-Directed Practice): Arabic transcription projects. Core strengths include Other. AI-training focus includes data types such as Audio and Text and labeling workflows including Transcription, Evaluation, and Rating.

Labeling Experience

Independent AI Trainer & Data Annotator (Self-Directed Practice): Arabic proofreading and text cleanup

OtherText

Performed Arabic proofreading and editorial annotation by reviewing Arabic texts to correct grammatical errors and spelling mistakes. Improved readability by auditing content and removing stylistic weaknesses. Produced cleaner text that can be used to enhance training data quality for language models. • Corrected grammar and spelling in Arabic text • Enhanced readability and overall text flow • Reduced stylistic inconsistencies and weak phrasing • Prepared improved text versions for downstream use

2025 - Present

Independent AI Trainer & Data Annotator (Self-Directed Practice): LLM response evaluations (RLHF)

OtherText

Conducted RLHF-style evaluation of multiple LLM responses by comparing outputs for factual accuracy, logic gaps, and linguistic quality. Selected the most accurate, context-aware, and clear responses to support better model behavior. Applied guideline compliance practices to ensure evaluations followed consistent criteria. • Assessed responses for accuracy and logical consistency • Evaluated clarity and context awareness in outputs • Identified linguistic quality issues affecting usability • Compared multiple model responses to choose best candidates

2025 - Present

Independent AI Trainer & Data Annotator (Self-Directed Practice): Arabic transcription projects

OtherAudioTranscription

Independently performed Arabic transcription work by converting diverse Arabic audio and dialect recordings into written text. Ensured strict adherence to punctuation and spelling rules while producing clean, fully punctuated text suitable for AI training datasets. Validated and iterated on transcription outputs through self-led checks and experimental runs. • Cleaned and formatted transcribed text for dataset readiness • Applied punctuation/spelling standards during transcription • Produced dialect-inclusive transcriptions for linguistic coverage • Iterated based on transcription quality observations

2025 - Present

Education

Work History