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Omar Zieneldien

Omar Zieneldien

Medical Student & AI Data Training Specialist | RLHF, SFT & Clinical NLP Evaluation Expert

Egypt flagAlexandria, Egypt
$20.00/hrIntermediateLabelboxMercorOneforma

Key Skills

Software

LabelboxLabelbox
MercorMercor
OneFormaOneForma
TelusTelus
SuperAnnotateSuperAnnotate

Top Subject Matter

Healthcare & Clinical Medicine
Artificial Intelligence & Machine Learning
Data Annotation & AI Safety &AI Search Evaluation

Top Data Types

TextText
ImageImage
AudioAudio

Top Task Types

Text GenerationText Generation
Question AnsweringQuestion Answering
RLHFRLHF
TranscriptionTranscription
Prompt + Response Writing (SFT)Prompt + Response Writing (SFT)
Evaluation/RatingEvaluation/Rating
Text SummarizationText Summarization
Object DetectionObject Detection
Bounding BoxBounding Box
Entity (NER) ClassificationEntity (NER) Classification
Fine-tuningFine-tuning

Freelancer Overview

AI Search Evaluation & Content Analysis. Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include N and A. Education includes Bachelor of Medicine and Bachelor of Surgery, Alexandria Faculty of Medicine and High School Diploma, Victoria College. AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

IntermediateEnglishArabic

Labeling Experience

Multilingual AI Response Evaluation & Data Annotation (Arabic–English)

TextRLHF
Worked on evaluating and improving AI-generated responses as part of reinforcement learning with human feedback (RLHF) workflows. Assessed outputs for factual accuracy, relevance, coherence, and safety, ensuring alignment with human expectations. Ranked multiple responses to identify the best-performing outputs and contributed to refining model behavior. Identified hallucinations, bias, and inconsistencies, particularly in complex domains such as medical queries, while strictly adhering to annotation guidelines and maintaining high consistency.

Worked on evaluating and improving AI-generated responses as part of reinforcement learning with human feedback (RLHF) workflows. Assessed outputs for factual accuracy, relevance, coherence, and safety, ensuring alignment with human expectations. Ranked multiple responses to identify the best-performing outputs and contributed to refining model behavior. Identified hallucinations, bias, and inconsistencies, particularly in complex domains such as medical queries, while strictly adhering to annotation guidelines and maintaining high consistency.

2025 - Present

AI Search Evaluation & Content Analysis

Text
I evaluated AI-generated responses for factual accuracy, completeness, and practical usefulness across simulated search and browsing tasks. My work involved identifying hallucinations, misleading claims, and reasoning errors, and providing structured, evidence-based feedback. I maintained rigorous consistency using structured guidelines, contributing to rubric development and prompt writing tasks. • Assessed the quality of search queries and AI tool usage in text-based tasks. • Compared multiple model outputs side-by-side, documenting justifications for each decision. • Supported edge-case identification and contributed to prompt/rubric design. • Synthesized findings to help enhance real-world AI performance and reliability.

I evaluated AI-generated responses for factual accuracy, completeness, and practical usefulness across simulated search and browsing tasks. My work involved identifying hallucinations, misleading claims, and reasoning errors, and providing structured, evidence-based feedback. I maintained rigorous consistency using structured guidelines, contributing to rubric development and prompt writing tasks. • Assessed the quality of search queries and AI tool usage in text-based tasks. • Compared multiple model outputs side-by-side, documenting justifications for each decision. • Supported edge-case identification and contributed to prompt/rubric design. • Synthesized findings to help enhance real-world AI performance and reliability.

Present

Clinical Document Annotation & Entity Extraction

DocumentEntity Ner Classification
Annotated clinical and medical documents to extract and classify key entities such as diagnoses, medications, symptoms, and laboratory findings. Applied named entity recognition (NER) techniques to structure unstructured medical text into organized datasets suitable for AI training. Ensured high accuracy and consistency by following detailed annotation guidelines and standardizing entity definitions across the dataset. Handled complex medical terminology and ambiguous cases by applying domain knowledge and contextual understanding. Contributed to improving dataset quality by identifying inconsistencies, refining labeling rules, and ensuring precise entity classification for downstream NLP tasks.

Annotated clinical and medical documents to extract and classify key entities such as diagnoses, medications, symptoms, and laboratory findings. Applied named entity recognition (NER) techniques to structure unstructured medical text into organized datasets suitable for AI training. Ensured high accuracy and consistency by following detailed annotation guidelines and standardizing entity definitions across the dataset. Handled complex medical terminology and ambiguous cases by applying domain knowledge and contextual understanding. Contributed to improving dataset quality by identifying inconsistencies, refining labeling rules, and ensuring precise entity classification for downstream NLP tasks.

2024 - 2025

Medical AI Prompt–Response Authoring & Fine-Tuning

TextPrompt Response Writing SFT
Developed high-quality prompt–response pairs for supervised fine-tuning (SFT) of AI models, focusing on realistic user queries within medical and general knowledge domains. Crafted structured, accurate, and context-aware responses that demonstrate strong instruction-following, clarity, and safe communication. Ensured responses were factually correct and aligned with domain best practices, while remaining accessible to non-expert users. Reviewed and refined prompts to eliminate ambiguity and improve dataset consistency. Applied critical evaluation to detect and prevent hallucinations, misinformation, and unsafe outputs, maintaining strict adherence to annotation and safety guidelines.

Developed high-quality prompt–response pairs for supervised fine-tuning (SFT) of AI models, focusing on realistic user queries within medical and general knowledge domains. Crafted structured, accurate, and context-aware responses that demonstrate strong instruction-following, clarity, and safe communication. Ensured responses were factually correct and aligned with domain best practices, while remaining accessible to non-expert users. Reviewed and refined prompts to eliminate ambiguity and improve dataset consistency. Applied critical evaluation to detect and prevent hallucinations, misinformation, and unsafe outputs, maintaining strict adherence to annotation and safety guidelines.

2024 - 2025

AI Response Evaluation & Quality Rating

TextEvaluation Rating
Evaluated AI-generated responses across a wide range of prompts, focusing on assessing quality based on accuracy, relevance, coherence, and instruction adherence. Assigned detailed evaluation ratings to compare outputs and determine the most effective responses. Identified common model issues such as hallucinations, incomplete answers, and logical inconsistencies. Applied structured reasoning to justify ratings and ensure consistency across large datasets. Contributed to improving overall model performance by highlighting weaknesses and patterns in low-quality outputs. Maintained strict compliance with annotation guidelines while handling ambiguous and edge-case scenarios, ensuring reliable and high-quality evaluation results.

Evaluated AI-generated responses across a wide range of prompts, focusing on assessing quality based on accuracy, relevance, coherence, and instruction adherence. Assigned detailed evaluation ratings to compare outputs and determine the most effective responses. Identified common model issues such as hallucinations, incomplete answers, and logical inconsistencies. Applied structured reasoning to justify ratings and ensure consistency across large datasets. Contributed to improving overall model performance by highlighting weaknesses and patterns in low-quality outputs. Maintained strict compliance with annotation guidelines while handling ambiguous and edge-case scenarios, ensuring reliable and high-quality evaluation results.

2023 - 2024

Education

V

Victoria College

High School Diploma, General Studies

High School Diploma
Not specified
A

Alexandria Faculty of Medicine

Bachelor of Medicine and Bachelor of Surgery, Medicine and Surgery

Bachelor of Medicine and Bachelor of Surgery
Not specified

Work History

A

Alexandria Faculty of Medicine

Medical Intern

Alexandria
2023 - Present