Alex Safe

Key Skills

Software

Labelbox

Label Studio

OneForma

Remotasks

Scale AI

Toloka

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Document

Image

Text

Video

Top Label Types

Bounding Box

Classification

Cuboid

Data Collection

Entity Ner Classification

Evaluation Rating

Prompt Response Writing SFT

Question Answering

Segmentation

Text Generation

Transcription

Translation Localization

Freelancer Overview

I am an experienced AI data specialist with over five years supporting a wide range of data annotation, labeling, and evaluation projects for machine learning and AI systems. My expertise includes annotating text, images, and audio, reviewing LLM-generated responses, assessing search relevance, and performing quality assurance across diverse domains such as natural language processing and content moderation. I am highly skilled at following detailed guidelines, maintaining accuracy, and providing constructive feedback to improve data quality and model performance. I am proficient with tools like Microsoft Excel, Google Sheets, and basic Python for data analysis, and have a proven track record working remotely with international teams on platforms including Remotasks, Appen, Clickworker, and Prolific. My strong language abilities in English, Swahili, and Oromo, combined with my analytical mindset and commitment to high standards, enable me to consistently deliver reliable, high-quality results for AI training data projects.

Labeling Experience

Multilingual Text Annotation for AI NLP models

Label StudioTextBounding BoxText Generation

The project involved annotating 15,000+ social media responses in English to create a high quality dataset for training NLP models in sentiment analysis, intent detection, and named entity recognition. The goal was to help AiI systems understand customer opinions, detect intents, and extract relevant entities across languages and cultural contexts. I specifically performed data labeling tasks like annotated posts for sentiment: positive, negative, neutral. Categorized text for intent: informational, complaint, praise or transactional. I also performed Named Entity Recognition to identify people, organizations, locations and products. I also handled multilingual content, ensuring annotations reflected language nuances, slang, and cultural context. Total annotated data was over 15,000 social media responses in English and Swahili for a period of 2 months. I achieved 95% Annotation accuracy through cross review and verification processes to deliver high quality dataset for AI NLP training

The project involved annotating 15,000+ social media responses in English to create a high quality dataset for training NLP models in sentiment analysis, intent detection, and named entity recognition. The goal was to help AiI systems understand customer opinions, detect intents, and extract relevant entities across languages and cultural contexts. I specifically performed data labeling tasks like annotated posts for sentiment: positive, negative, neutral. Categorized text for intent: informational, complaint, praise or transactional. I also performed Named Entity Recognition to identify people, organizations, locations and products. I also handled multilingual content, ensuring annotations reflected language nuances, slang, and cultural context. Total annotated data was over 15,000 social media responses in English and Swahili for a period of 2 months. I achieved 95% Annotation accuracy through cross review and verification processes to deliver high quality dataset for AI NLP training

2019

Education

Work History