Oluwafemi O.

Key Skills

Software

CVAT

Labelbox

Top Subject Matter

LLM Evaluation

Text Generation

Safety Domain Expertise

Top Data Types

Text

Image

Top Task Types

RLHF

Bounding Box

Prompt + Response Writing (SFT)

Entity (NER) Classification

Freelancer Overview

AI Generalist & Data Contributor. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and CVAT. Education includes Bachelor of Science, Olabisi Onabanjo University. AI-training focus includes data types such as Text and Image and labeling workflows including Evaluation, Rating, and RLHF.

Labeling Experience

Text Annotation for NLP & Entity Labeling

LabelboxTextEntity (NER) Classification

Performed text annotation and entity classification to support NLP dataset labeling for AI language model comprehension and training. Labeled and analyzed intent, tone, and linguistic features across diverse text samples, adapting annotation criteria as project requirements evolved. Implemented rubric updates and feedback loops to continuously improve data quality standards. • Conducted NER labeling and intent classification projects for NLP pipelines. • Adjusted annotation guidelines for stylistic and functional consistency. • Supported rubric and QA updates to enhance annotation efficiency. • Worked with annotation and evaluation teams to identify and address data quality issues.

2024 - Present

Prompt Engineer & Language Model Support

TextPrompt + Response Writing (SFT)

Engineered, tested, and optimized prompts for AI language model outputs across various use cases including creative, technical, and conversational domains. Designed stylistic guidelines and tone adaptation frameworks to align AI-generated text with precise project requirements. Provided structured feedback that guided improvements in language generation behavior. • Created and iteratively refined prompts for model probing and evaluation. • Implemented tone and style adaptations for text generation tasks. • Evaluated and summarized model outputs for adherence to defined criteria. • Delivered continuous feedback to guide LLM training dataset evolution.

2024 - Present

LLM Evaluator & RLHF Specialist

TextRLHF

Contributed human feedback to AI systems as part of reinforcement learning from human feedback (RLHF) processes. Provided structured, comprehensive feedback on instruction-following, factual accuracy, and output tone. Participated in calibration sessions to align scoring and escalation standards across a distributed annotation team. • Evaluated model responses for grammar, tone, and adherence to style guides. • Provided actionable feedback to refine AI alignment and training data quality. • Used Cohen's Kappa and Krippendorff's Alpha frameworks to ensure scoring reliability. • Implemented and followed established escalation and QA frameworks.

2024 - Present

AI Generalist & Data Contributor

Text

Evaluated AI language model outputs for accuracy, logical consistency, and alignment with safety criteria across a variety of tasks. Designed and calibrated structured rubrics and scoring methods to ensure annotation quality at scale. Authored detailed justifications for model preferences and contributed to team calibration and instruction-following quality improvement initiatives. • Ranked model outputs using HHH (Helpfulness, Honesty, Harmlessness) criteria in side-by-side comparisons. • Detected hallucinations, factual inaccuracies, and nuanced logical fallacies in generated text. • Developed and refined prompts, tone guidelines, and stylistic parameters for language model training. • Implemented bias prevention and validation workflows during LLM training data curation.

2024 - Present

Annotation QA Calibration Framework - Personal Project

Text

Designed and deployed a quality assurance and calibration framework tailored for annotation tasks involving multiple human evaluators. Integrated IAA measurement and structured disagreement resolution steps to ensure consistent annotation quality. Developed calibration checklists and escalation protocols to streamline multi-annotator workflows. • Created structured rubrics for annotator alignment. • Integrated calibration checklists into routine annotation QA. • Enabled IAA tracking using Cohen's Kappa metrics. • Facilitated escalation to resolve inter-annotator disagreements.

2025 - 2025

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Education

Work History