Ganiyu Ogundana

Key Skills

Software

Don't disclose

Top Subject Matter

AI-generated content

Technical responses

AI Agent Security

Top Data Types

Text

Image

Audio

Top Task Types

Red Teaming

Prompt + Response Writing (SFT)

Text Generation

Fine-tuning

Evaluation/Rating

Text Summarization

Question Answering

RLHF

Classification

Bounding Box

Transcription

Object Detection

Freelancer Overview

AI Content Reviewer & Data Annotator (Remote) at Telus Data AI. Brings 7+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Telus and Other. Education includes Bachelor of Science, Cyprus International University (2019). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Red Teaming.

Labeling Experience

AI Content Reviewer & Data Annotator (Remote) at Telus Data AI

TelusText

I reviewed and annotated AI-generated and technical text for model training and performance improvement. I applied rubrics and taxonomies to ensure accuracy and clarity of datasets, delivering ground truth outputs for supervised learning processes. I flagged data inconsistencies, ambiguities, and errors while generating high-quality feedback and documentation. • Evaluated logical soundness, factual accuracy, and bias in AI outputs. • Created and refined prompt sets and evaluation criteria for continual model enhancement. • Used Notion and Google Sheets to report findings and support asynchronous collaboration. • Handled high weekly data volumes across multiple technical subject domains.

I reviewed and annotated AI-generated and technical text for model training and performance improvement. I applied rubrics and taxonomies to ensure accuracy and clarity of datasets, delivering ground truth outputs for supervised learning processes. I flagged data inconsistencies, ambiguities, and errors while generating high-quality feedback and documentation. • Evaluated logical soundness, factual accuracy, and bias in AI outputs. • Created and refined prompt sets and evaluation criteria for continual model enhancement. • Used Notion and Google Sheets to report findings and support asynchronous collaboration. • Handled high weekly data volumes across multiple technical subject domains.

2023 - 2026

AI-Agent-Security Project (Red Teaming Annotator)

OtherTextRed Teaming

I developed red-teaming prompts and annotated large language model outputs to evaluate AI agent security. I tested and documented vulnerabilities by generating adversarial examples using PyRIT and Garak. I ensured assessment quality by following established red-teaming guidelines. • Focused on evaluating generative AI models for security edge cases. • Utilized PyRIT and Garak frameworks for comprehensive red teaming tasks. • Generated and labeled adversarial prompt-response pairs. • Provided structured security evaluation feedback for engineering teams.

I developed red-teaming prompts and annotated large language model outputs to evaluate AI agent security. I tested and documented vulnerabilities by generating adversarial examples using PyRIT and Garak. I ensured assessment quality by following established red-teaming guidelines. • Focused on evaluating generative AI models for security edge cases. • Utilized PyRIT and Garak frameworks for comprehensive red teaming tasks. • Generated and labeled adversarial prompt-response pairs. • Provided structured security evaluation feedback for engineering teams.

2025 - 2025

Prompt Engineering & Annotation Library Project

OtherTextPrompt Response Writing SFT

I created and labeled over 800 prompt-response pairs for large language model (LLM) supervised training. I synthesized instructions and responses for diverse domains to support internal model improvement initiatives. I collaborated with multiple teams to align prompts with desired behaviors for SFT objectives. • Authored prompts and responses to target specific use cases and tasks. • Labeled and organized data for supervised LLM training. • Coordinated improvements to domain coverage and intent variety. • Supported multi-team adoption of annotated datasets for internal use.

I created and labeled over 800 prompt-response pairs for large language model (LLM) supervised training. I synthesized instructions and responses for diverse domains to support internal model improvement initiatives. I collaborated with multiple teams to align prompts with desired behaviors for SFT objectives. • Authored prompts and responses to target specific use cases and tasks. • Labeled and organized data for supervised LLM training. • Coordinated improvements to domain coverage and intent variety. • Supported multi-team adoption of annotated datasets for internal use.

2024 - 2024

Education

Work History