Ahmed Khaled

Key Skills

Software

Appen

Clickworker

CVAT

Data Annotation Tech

Label Studio

Lionbridge

OneForma

Remotasks

SuperAnnotate

Toloka

Telus

Other

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Document

Image

Text

Video

Top Task Types

Audio Recording

Classification

Data Collection

Evaluation Rating

Prompt Response Writing SFT

Question Answering

Segmentation

Text Generation

Text Summarization

Transcription

Translation Localization

Freelancer Overview

I am an experienced data annotator and AI trainer with over three years of hands-on expertise in text, image, audio, and LLM data annotation across diverse global projects. I have contributed to high-impact AI initiatives with leading companies such as Telus, Welocalize, RWS, DataAnnotation, Outlier, OneForma, Invisible, Appen, and more, working on tasks ranging from multi-turn dialogue evaluation and content moderation to OCR, transcription, and translation. My strengths include delivering high-accuracy annotation QA (98–99%), mentoring project teams, and ensuring data privacy compliance. I am skilled with tools like Labelbox, Super Annotate, Human Signal, CVAT, and AWS S3, and am comfortable collaborating with remote, multicultural teams. I am fully available for relocation and onsite roles, ready to support AI development in any domain requiring precise, reliable training data.

Labeling Experience

Expert Text Data Annotator

AppenTextQuestion AnsweringPrompt Response Writing SFT

The Bable Project involved evaluating LLM prompts and responses, focusing on text data annotation. Tasks included rating accuracy, refining prompts, and ensuring response quality. The project size was approximately 500 datasets, with strict quality measures like double-checking for 98% accuracy adherence.

The Bable Project involved evaluating LLM prompts and responses, focusing on text data annotation. Tasks included rating accuracy, refining prompts, and ensuring response quality. The project size was approximately 500 datasets, with strict quality measures like double-checking for 98% accuracy adherence.

2025 - 2025

Advanced AI Data Trainer

CVATVideoEvaluation RatingData Collection

Worked as an Advanced AI Data Trainer on an LLM training project focused on improving model safety, helpfulness, and instruction-following. Labeled and evaluated video-based content for action/object understanding and response quality, including classification, object detection support, and detailed evaluation/rating of model outputs against predefined rubrics. Used CVAT to review frames, apply consistent labels, and verify annotation accuracy. Ensured high-quality delivery through strict guideline adherence, edge-case handling, and systematic QA (self-checks, spot checks, and consistency reviews). Collaborated with project leads to resolve ambiguities, document labeling decisions, and maintain alignment with evolving project policies and quality standards across large-scale batches.

Worked as an Advanced AI Data Trainer on an LLM training project focused on improving model safety, helpfulness, and instruction-following. Labeled and evaluated video-based content for action/object understanding and response quality, including classification, object detection support, and detailed evaluation/rating of model outputs against predefined rubrics. Used CVAT to review frames, apply consistent labels, and verify annotation accuracy. Ensured high-quality delivery through strict guideline adherence, edge-case handling, and systematic QA (self-checks, spot checks, and consistency reviews). Collaborated with project leads to resolve ambiguities, document labeling decisions, and maintain alignment with evolving project policies and quality standards across large-scale batches.

2025 - 2025

Education

Work History