Clifford O.

Key Skills

Software

Appen

Axiom AI

Clickworker

Anno-Mage

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Task Types

Classification

Polygon

Entity (NER) Classification

Bounding Box

Freelancer Overview

CLIFFORD ONYANGO AI Training Specialist · Data Annotation Expert · Technical Writer [email protected] | (+254) 704385889 P R O F E S S I O N A L S U M M A R Y Detail-oriented AI training specialist with 3–5 years of hands-on experience in data annotation, prompt engineering, and technical writing. Skilled at evaluating large language model outputs for accuracy, coherence, safety, and alignment with human intent. Brings a rare combination of software development, academic research, and editorial precision — enabling high-quality contributions across every stage of the AI training pipeline. C O R E C O M P E T E N C I E S AI & ML: Prompt Engineering · RLHF · LLM Annotation: Text · Code · Reasoning · Evaluation · NLP Multimodal Labelling Programming: Python · JavaScript · SQL · Writing: Technical Docs · SOP Creation · Bash · Git Style Guides Research: Academic Analysis · Literature Tools: Label Studio · Scale AI · Surge AI · Review · Reporting Notion P R O F E S S I O N A L E X P E R I E N C E AI Data Annotation Specialist · Freelance / Remote Jan 2022 – Present | Remote Annotated 10,000+ text, code, and reasoning samples for LLM fine-tuning and RLHF pipelines ◆ across multiple clients. Evaluated AI-generated responses for factual accuracy, tone, safety, and logical consistency, ◆ producing structured comparative feedback reports. Collaborated with prompt engineers to design and refine instruction datasets for supervised fine- ◆ tuning (SFT). Maintained a 97%+ quality

Labeling Experience

AI & ML: Prompt Engineering · RLHF · LLM Evaluation · NLP

VideoRLHF

Annotated 10,000+ text, code, and reasoning samples for LLM fine-tuning and RLHF pipelines across multiple clients. ◆ Evaluated AI-generated responses for factual accuracy, tone, safety, and logical consistency, producing structured comparative feedback reports. ◆ Collaborated with prompt engineers to design and refine instruction datasets for supervised fine tuning (SFT). ◆ Maintained a 97%+ quality agreement score across inter-annotator reliability checks on all projects. ◆ Authored annotation guidelines and quality rubrics adopted by distributed teams of up to 20 reviewers.

2021 - 2022

Data Annotation

ImageClassification

Authored a 40-page annotation standards handbook later adopted across a 15-person distributed annotation team. Systematically reviewed 500 AI responses for demographic bias and produced a rubric report incorporated into a model evaluation framework.

2016 - 2021

Education