Adams Atuti

Key Skills

Software

Data Annotation Tech

Remotasks

Top Subject Matter

No subject matter listed

Top Data Types

Text

Top Label Types

Evaluation Rating

Data Collection

Freelancer Overview

I am a native Swahili linguist and AI language specialist with over two years of hands-on experience in data annotation, LLM evaluation, and multilingual content analysis. My expertise lies in Swahili grammar, morphology, syntax, semantics, and pragmatic features, as well as C1-level English fluency. I have worked extensively on linguistic data labeling, conversational AI prompt annotation, and the evaluation of large language models for grammatical accuracy, logical reasoning, and cultural nuance. My work includes structured reporting of error traces, prompt engineering feedback, and maintaining high annotation accuracy using tools like polygon and QA review systems. I am skilled in translation, localization, and the creation of training datasets for NLP and speech recognition projects, with a strong focus on detail, consistency, and cultural sensitivity. I thrive in remote, contract-based roles and am committed to enhancing AI robustness through meticulous data annotation and evaluation AI Data Annotator & Model Reviewer (Freelance – Remote) • Evaluated AI-generated responses for clarity, factual accuracy, and policy compliance. • Labeled and classified datasets to improve machine learning model performance. • Applied structured annotation guidelines with consistency and precision. • Identified hallucinations, logical errors, and bias in AI outputs. • Maintained productivity in deadline-driven remote environments

Labeling Experience

AI Data Annotator & Model Reviewer (Freelance – Remote)

Data Annotation TechTextEvaluation RatingData Collection

This role involved evaluating and labeling AI-generated text responses using structured guidelines. Tasks included assessing factual accuracy, clarity, and compliance with content policies. The work supported training and improving machine learning and large language models. • Judged AI outputs for logical consistency and bias. • Applied annotation and classification procedures to text datasets. • Reviewed outputs for hallucinations, errors, and policy violations. • Maintained productivity in a remote and deadline-driven environment.

This role involved evaluating and labeling AI-generated text responses using structured guidelines. Tasks included assessing factual accuracy, clarity, and compliance with content policies. The work supported training and improving machine learning and large language models. • Judged AI outputs for logical consistency and bias. • Applied annotation and classification procedures to text datasets. • Reviewed outputs for hallucinations, errors, and policy violations. • Maintained productivity in a remote and deadline-driven environment.

2025

Education

Work History