Mohamed Hili M.

Key Skills

Software

Don't disclose

Top Subject Matter

Language / Writing - English grammar & writing quality Content clarity and rewriting

Technology & AI - AI chatbot conversations Prompt-response evaluation General LLM training data

Business & Technical Documentation - Technical manuals Engineering reports SOPs (standard operating procedures) Product documentation

Top Data Types

Text

Document

Audio

Top Task Types

Bounding Box

Polygon

Segmentation

Classification

Entity (NER) Classification

Point/Key Point

Polyline

Cuboid

Object Detection

Text Generation

Question Answering

Text Summarization

RLHF

Transcription

Fine-tuning

Red Teaming

Evaluation/Rating

Data Collection

Function Calling

Prompt + Response Writing (SFT)

Freelancer Overview

I am a Mechanical Engineer with experience working in technical environments that require strong attention to detail, analytical thinking, and data accuracy. Through my engineering studies and professional experience, I have developed skills in reviewing technical documentation, validating information, conducting research, and maintaining high quality standards when working with complex data and specifications. I am proficient in English (TOEIC 905/990) and comfortable working with structured guidelines, spreadsheets, and digital tools. I am currently expanding my expertise into AI training and data annotation. My background has equipped me with the ability to identify inconsistencies, follow detailed instructions, evaluate information critically, and produce accurate written content. I am particularly interested in AI evaluation, data labeling, prompt engineering, and quality assurance tasks, where I can apply my problem-solving skills, attention to detail, and commitment to delivering high-quality work.

Labeling Experience

AI Data Annotation Specialist

TextEntity (NER) Classification

Annotated and validated multi-modal datasets using Label Studio, with a focus on text classification, named entity recognition (NER), image classification, and multi-label tagging workflows designed for machine learning training and evaluation. Key Contributions Labeled 1,000+ text samples for sentiment analysis and topic classification using predefined annotation guidelines. Annotated 500+ entities (people, organizations, locations) across news and social media datasets for NER tasks. Classified 300+ images into object categories and verified label consistency across batches. Created detailed annotation guidelines to reduce ambiguity and improve inter-task consistency.

2026 - 2026

Education

Work History