Fred Mugo

Key Skills

Software

Data Annotation Tech

Appen

Clickworker

CVAT

Humanatic

Img Lab

Mindrift

Remotasks

Top Subject Matter

Large Language Models (LLMs)

AI Ethics & Safety

Multi-step Reasoning

Top Data Types

Text

Audio

Image

Top Task Types

Classification

Object Detection

Question Answering

Text Summarization

Transcription

Evaluation Rating

Data Collection

Computer Programming Coding

Text Generation

Freelancer Overview

Lead AI Content Evaluator. Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Data Annotation Tech and Appen. Education includes Bachelor of Arts, University of British Columbia. AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

Lead AI Content Evaluator

Data Annotation TechText

As Lead AI Content Evaluator at DataAnnotation.tech, I evaluated Large Language Model (LLM) outputs for logical coherence, safety, and helpfulness. I specialized in auditing chain-of-thought responses and identifying hallucinations, as well as conducting adversarial red teaming to ensure robust model behavior. I collaborated with stakeholders to refine annotation guidelines for new model behaviors. • Evaluated LLM outputs for consistency, factuality, and safety • Applied adversarial red teaming to test content security boundaries • Improved annotation guidelines via asynchronous collaboration • Identified and reported subtle model hallucinations for further mitigation

As Lead AI Content Evaluator at DataAnnotation.tech, I evaluated Large Language Model (LLM) outputs for logical coherence, safety, and helpfulness. I specialized in auditing chain-of-thought responses and identifying hallucinations, as well as conducting adversarial red teaming to ensure robust model behavior. I collaborated with stakeholders to refine annotation guidelines for new model behaviors. • Evaluated LLM outputs for consistency, factuality, and safety • Applied adversarial red teaming to test content security boundaries • Improved annotation guidelines via asynchronous collaboration • Identified and reported subtle model hallucinations for further mitigation

2024 - Present

Data Specialist & Swahili Consultant

AppenTextClassification

As a Data Specialist and Swahili Consultant at Appen, I managed the entire data annotation workflow for Swahili NLU projects. I verified and corrected over 50,000 data points for speech recognition and text-to-speech models, maintaining high quality standards. I also authored technical documentation to standardize annotation practices across teams. • Managed workflow for annotation and verification of Swahili language datasets • Performed quality control to achieve a 99.2% accuracy rate • Developed guidelines for annotator consistency on multilingual projects • Utilized Jira/Trello to coordinate annotation activities and deadlines

As a Data Specialist and Swahili Consultant at Appen, I managed the entire data annotation workflow for Swahili NLU projects. I verified and corrected over 50,000 data points for speech recognition and text-to-speech models, maintaining high quality standards. I also authored technical documentation to standardize annotation practices across teams. • Managed workflow for annotation and verification of Swahili language datasets • Performed quality control to achieve a 99.2% accuracy rate • Developed guidelines for annotator consistency on multilingual projects • Utilized Jira/Trello to coordinate annotation activities and deadlines

2022 - 2023

Education

Work History