Olohimai Oladipupo

Key Skills

Software

Labelbox

Top Subject Matter

Conversational AI

Nlp Domain Expertise

Multimodal AI

Top Data Types

Text

Image

Document

Top Task Types

RLHF

Entity Ner Classification

Relationship

Freelancer Overview

AI Trainer / Data Annotation Specialist. Core strengths include Labelbox, N, and A. Education includes Bachelor of Arts, University of Ilorin (2018). AI-training focus includes data types such as Text and Image and labeling workflows including RLHF, Evaluation, and Rating.

Labeling Experience

AI Trainer / Data Annotation Specialist

LabelboxTextRLHF

In this role, I evaluated and ranked AI-generated responses, offering structured human feedback to refine conversational AI outputs. I annotated and labelled high-volume datasets across text, image, and audio modalities for machine learning pipelines. I applied detailed annotation taxonomies to ensure dataset consistency and contributed to model improvement through edge case identification. • Utilized prompt-response analysis to identify weaknesses in model reasoning and factual accuracy. • Exceeded quality benchmarks while completing tasks efficiently under tight deadlines. • Collaborated with distributed teams to improve annotation workflows. • Participated in onboarding and calibration tasks to ensure project alignment.

In this role, I evaluated and ranked AI-generated responses, offering structured human feedback to refine conversational AI outputs. I annotated and labelled high-volume datasets across text, image, and audio modalities for machine learning pipelines. I applied detailed annotation taxonomies to ensure dataset consistency and contributed to model improvement through edge case identification. • Utilized prompt-response analysis to identify weaknesses in model reasoning and factual accuracy. • Exceeded quality benchmarks while completing tasks efficiently under tight deadlines. • Collaborated with distributed teams to improve annotation workflows. • Participated in onboarding and calibration tasks to ensure project alignment.

2024 - Present

AI Content Reviewer / Moderator

Text

As an AI Content Reviewer / Moderator, I reviewed AI-generated and user-generated content for safety, compliance, and quality. I flagged harmful or biased outputs, supporting safer AI deployment. My work required nuanced judgment and high throughput while maintaining accuracy and consistency. • Applied complex guidelines to assess ambiguous or sensitive content. • Maintained rigorous accuracy in content review and policy enforcement. • Supported ongoing model improvements by providing high-quality feedback. • Contributed to a safer digital environment for end users.

As an AI Content Reviewer / Moderator, I reviewed AI-generated and user-generated content for safety, compliance, and quality. I flagged harmful or biased outputs, supporting safer AI deployment. My work required nuanced judgment and high throughput while maintaining accuracy and consistency. • Applied complex guidelines to assess ambiguous or sensitive content. • Maintained rigorous accuracy in content review and policy enforcement. • Supported ongoing model improvements by providing high-quality feedback. • Contributed to a safer digital environment for end users.

2023 - 2024

Multimodal AI Training Project

ImageRelationship

During the Multimodal AI Training Project, I annotated image-text pairs to advance model understanding of visual-contextual relationships. This improved the AI's performance in multimodal comprehension tasks. My meticulous labeling strengthened the integration of visual and textual data for AI systems. • Addressed complex multimodal data challenges. • Ensured precision in annotating aligned datasets. • Advanced cross-modal research and application. • Contributed to improved data pipelines and model performance.

During the Multimodal AI Training Project, I annotated image-text pairs to advance model understanding of visual-contextual relationships. This improved the AI's performance in multimodal comprehension tasks. My meticulous labeling strengthened the integration of visual and textual data for AI systems. • Addressed complex multimodal data challenges. • Ensured precision in annotating aligned datasets. • Advanced cross-modal research and application. • Contributed to improved data pipelines and model performance.

Not specified

NLP Data Annotation Project

TextEntity Ner Classification

In the NLP Data Annotation Project, I labelled datasets used for tasks such as sentiment analysis, intent classification, and entity recognition. I ensured high annotation consistency by applying complex labelling schemas and robust quality control. My accurate data labeling enabled effective training for NLP models. • Supported multiple natural language processing tasks. • Maintained strict guideline adherence to ensure data integrity. • Played a key role in high-quality dataset creation. • Helped optimize annotation standards for future projects.

In the NLP Data Annotation Project, I labelled datasets used for tasks such as sentiment analysis, intent classification, and entity recognition. I ensured high annotation consistency by applying complex labelling schemas and robust quality control. My accurate data labeling enabled effective training for NLP models. • Supported multiple natural language processing tasks. • Maintained strict guideline adherence to ensure data integrity. • Played a key role in high-quality dataset creation. • Helped optimize annotation standards for future projects.

Not specified

Conversational AI Evaluation Project

Text

For the Conversational AI Evaluation Project, I assessed chatbot responses for fluency, tone, reasoning, and factual correctness. Structured feedback provided by me contributed to improved dialogue quality and user satisfaction. My evaluations helped shape project guidelines and model tuning strategies. • Focused on both user experience and system performance. • Incorporated comprehensive linguistic detail and analytic rigor. • Helped establish best practices for conversational evaluation. • Ensured annotation consistency across evaluation cycles.

For the Conversational AI Evaluation Project, I assessed chatbot responses for fluency, tone, reasoning, and factual correctness. Structured feedback provided by me contributed to improved dialogue quality and user satisfaction. My evaluations helped shape project guidelines and model tuning strategies. • Focused on both user experience and system performance. • Incorporated comprehensive linguistic detail and analytic rigor. • Helped establish best practices for conversational evaluation. • Ensured annotation consistency across evaluation cycles.

Not specified

Education

Work History