Noelle O.

Key Skills

Software

Clickworker

Data Annotation Tech

Labelbox

Telus

Top Subject Matter

AI safety assurance

model governance

regulated-industry compliance

Top Data Types

Text

Document

Image

Top Task Types

Bounding Box

RLHF

Text Generation

Red Teaming

Prompt + Response Writing (SFT)

Transcription

Text Summarization

Freelancer Overview

AI Model Evaluation Specialist — Invisible Technologies. Brings 13+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Master of Business Administration, Abertay University (2026) and Bachelor of Arts (Honors), University of Stirling (2020). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Labeling Experience

Computer Systems Validation Specialist — Charles River Laboratories

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Performed compliance assurance and controls validation work that is directly applicable to AI product risk assessment and AI governance use cases. Conducted risk assessments and maintained a risk register for regulated enterprise software systems, translating validation findings into residual risk reporting for senior stakeholders. Supported audit readiness and inspection responses by preparing documentation and coordinating cross-functional evidence under regulatory scrutiny. • Risk assessment execution and risk register management for regulated systems • Compliance assurance reviews against regulatory/technical frameworks • Audit-ready documentation for external regulatory inspections • IAM control remediation and residual risk reporting

2024 - Present

AI Model Evaluation Specialist — Invisible Technologies

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Evaluated large language model outputs for knowledge accuracy, logical reasoning, and domain-specific compliance characteristics to support AI safety assurance and model governance. Used adversarial prompting and RLHF-style feedback mechanisms to identify model failure modes and improve reliability in regulated-industry settings. Documented structured quality assessments to inform governance and development-layer improvements. • Knowledge accuracy and logical reasoning assessment of LLM outputs • Adversarial prompting to surface failure modes • RLHF feedback-style quality improvement loop • Compliance-focused evaluation aligned to regulated contexts

2024 - Present

Education

Work History