Chidiebere G.

Key Skills

Software

No software listed

Top Subject Matter

Cybersecurity (IAM, threat modeling, social engineering, incident response)

Web3/Blockchain risk communication and technical correctness

Legal Services & Contract Review

Top Data Types

Audio

Image

Text

Top Task Types

Red Teaming

Freelancer Overview

Cybersecurity Researcher & Lab Architect (Independent/Academic) — preparing rubric-driven RLHF/red-teaming evaluations. Brings 4+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Bachelor of Science, Landmark University (2029) and Certified in Cybersecurity, ISC2. AI-training focus includes data types such as Text and Document and labeling workflows including Red Teaming, Evaluation, and Rating.

Labeling Experience

Cybersecurity Researcher & Lab Architect (Independent/Academic) — preparing rubric-driven RLHF/red-teaming evaluations

TextRed Teaming

Developed and operated a virtual cybersecurity lab to simulate network attack scenarios for accurate, rubric-based evaluation of LLM outputs in adversarial contexts. Produced structured threat documentation and workflow reports that support consistent annotation judgments across high-volume cybersecurity datasets. Applied research on fraud and identity theft methods to assess whether AI-generated security guidance is accurate, safe, or harmful. • Simulated attack and manipulation scenarios relevant to adversarial LLM testing • Produced documentation to standardize evaluations and labeling criteria • Assessed harmfulness and safety of AI-generated cybersecurity content • Supports red-teaming prompt/response evaluation using domain expertise

2024 - Present

Web3 Technical Content Creator & Risk Communicator (Independent/Freelance) — evaluation-ready cybersecurity/Web3 content work

OtherDocument

Created precise Web3 and blockchain technical content that emphasizes factual accuracy, consistent terminology, and careful risk communication. Evaluated DeFi-related outputs for whether they are accurate, misleading, or potentially harmful, aligning with safety-focused AI training and preference evaluation needs. Maintained consistent tone and quality standards across multiple formats to support reliable dataset-wide judgment. • Produced standardized, technically accurate descriptions for blockchain contexts • Performed content risk assessment aligned with AI output safety review • Ensured consistency of terminology and claims across deliverables • Supports evaluation-style labeling for RLHF and instruction-following quality

2023 - 2024

Education

Work History