p
pauljof

Paul. O

@pauljof

AI Data Annotator Evaluator

Canada
Anglais, Français
Certaines informations sont présentées en anglais.
À propos de moi
I am a detail-oriented AI Data and Digital Media professional with extensive experience in AI data annotation, quality evaluation, and data analysis. I specialize in following complex guidelines to identify quality issues and deliver structured work in remote, deadline-driven environments. My background also spans graphic design, branding, and video editing, allowing me to combine technical data skills with creative production.... Plus d’infos

Compétences

p
pauljof
Paul. O
hors ligne • 
Temps de réponse moyen de 7 heures

Voir mes services

Annotation des Data
I will create a verified handshake ai account, outlier, mercor, for USA, canada, UK
De20 $US / projetPlus de détails

Expérience professionnelle

Outlier

Senior AI Data Trainer & Quality Evaluator

Outlier

Mar 2025 - Present • 1 yr 7 mos

Evaluate AI-generated responses and training data against project-specific quality guidelines. Review outputs for accuracy, relevance, consistency, and adherence to instructions. Identify errors and inconsistencies in AI content while applying detailed rubrics across assigned evaluation projects. Provide structured feedback to support AI model training and improvement in a remote environment.

NEOLIFE

Executive Manager

NEOLIFE • Temps plein

Jan 2024 - Present • 2 yrs 9 mos

Manage and coordinate day-to-day network marketing activities and client relationships. Communicate product and service information to prospective and existing customers. Develop and maintain professional relationships with clients and team members. Support team coordination, customer engagement, and business development activities using presentation and organization skills.

Atlas_Capture

Lead Data Annotation & QA Specialist

Atlas Capture

Jul 2024 - Dec 2024 • 5 mos

Supervised end-to-end data labeling and annotation workflows for computer vision and NLP datasets. Conducted quality assurance audits on annotated image and text data before model integration. Created standard operating procedures and rubric documentation to maintain dataset integrity. Streamlined validation processes to reduce labeling errors and improve turnaround times.