Ibram Ghali

Key Skills

Software

Appen

Google Cloud Vertex AI

Labelbox

Mercor

Mindrift

OneForma

Scale AI

Telus

Top Subject Matter

No subject matter listed

Top Data Types

Document

Geospatial Tiled Imagery

Text

Top Task Types

Classification

Data Collection

Evaluation Rating

Mapping

Text Generation

Freelancer Overview

I am an experienced AI Data Annotator and AI Training Data Specialist with over five years of hands-on experience supporting machine learning and large language model (LLM) development. My work spans text, image, and video data, with a strong focus on data quality, consistency, and guideline adherence. I have contributed to projects involving NLP tasks such as text classification, intent detection, summarization, sentiment analysis, and LLM response evaluation, ensuring outputs meet accuracy, relevance, and safety standards. In computer vision and multimodal projects, I have worked on image and video annotation, visual verification, and quality assurance tasks, including object tagging, bounding boxes, and frame-level reviews. I am highly detail-oriented, comfortable working with complex annotation guidelines, and experienced in providing structured feedback to improve model performance. My ability to adapt quickly to new tools, workflows, and quality benchmarks allows me to consistently deliver high-quality training data that supports reliable and scalable AI systems.

Labeling Experience

LLM Training & Text Annotation for NLP and Model Evaluation

OneformaTextClassificationText Generation

Contributed to large-scale AI training projects focused on improving the performance and reliability of large language models (LLMs). Responsibilities included annotating and reviewing text data for tasks such as intent classification, question answering, summarization, and prompt–response generation. Performed qualitative evaluation and rating of model outputs based on accuracy, relevance, coherence, and safety guidelines, including RLHF-style feedback.

Contributed to large-scale AI training projects focused on improving the performance and reliability of large language models (LLMs). Responsibilities included annotating and reviewing text data for tasks such as intent classification, question answering, summarization, and prompt–response generation. Performed qualitative evaluation and rating of model outputs based on accuracy, relevance, coherence, and safety guidelines, including RLHF-style feedback.

2025

Education

Work History