Emmanuel Olorunfemi

Key Skills

Software

Mindrift

Other

Appen

OneForma

Top Subject Matter

General AI/LLMs

Multilingual NLP/Translation

General AI & Data Quality

Top Data Types

Text

Document

Top Task Types

Red Teaming

Data Collection

Segmentation

Polygon

Text Summarization

RLHF

Fine-tuning

Transcription

Freelancer Overview

AI Model Evaluator, Content & Prompt Engineering. Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Mindrift, Other, and Appen. Education includes Doctor of Pharmacy, Federal University Oye-Ekiti (2024). AI-training focus includes data types such as Text and labeling workflows including Red Teaming, Evaluation, and Rating.

Labeling Experience

Freelance Technical Translator & Linguistic QA

OtherText

As a Freelance Technical Translator & Linguistic QA at Translated, I verified grounding, cultural, and contextual consistency in LLM outputs. I ensured prevention of translation hallucinations and validated retrieval-augmented responses within multilingual and technical datasets. The work contributed to workflow validation for globalized, multilingual AI deployments. • Checked LLM translations for factual and cultural accuracy • Evaluated fidelity of technical and e-commerce dataset retrievals • Implemented grounding verification to prevent AI output errors • Collaborated to refine quality control for multilingual data

As a Freelance Technical Translator & Linguistic QA at Translated, I verified grounding, cultural, and contextual consistency in LLM outputs. I ensured prevention of translation hallucinations and validated retrieval-augmented responses within multilingual and technical datasets. The work contributed to workflow validation for globalized, multilingual AI deployments. • Checked LLM translations for factual and cultural accuracy • Evaluated fidelity of technical and e-commerce dataset retrievals • Implemented grounding verification to prevent AI output errors • Collaborated to refine quality control for multilingual data

2024 - Present

AI Model Evaluator — Content & Prompt Engineering

MindriftTextRed Teaming

As an AI Model Evaluator at Mindrift, I conducted adversarial testing, red-teaming, and prompt engineering for advanced LLMs. I assessed reasoning reliability, logic chains, and detected hallucinations and security vulnerabilities in model outputs. Complex evaluation rubrics and structured feedback were used to harden model safety and document failure modes. • Challenged models with prompt injections and jailbreaks • Evaluated chain-of-thought logic and hallucination presence • Applied structured pass/fail rubrics for output evaluation • Suggested improvements to system prompts and guardrails

As an AI Model Evaluator at Mindrift, I conducted adversarial testing, red-teaming, and prompt engineering for advanced LLMs. I assessed reasoning reliability, logic chains, and detected hallucinations and security vulnerabilities in model outputs. Complex evaluation rubrics and structured feedback were used to harden model safety and document failure modes. • Challenged models with prompt injections and jailbreaks • Evaluated chain-of-thought logic and hallucination presence • Applied structured pass/fail rubrics for output evaluation • Suggested improvements to system prompts and guardrails

2024 - Present

Data Annotator — Advanced AI Training

OneformaText

As a Data Annotator at OneForma, I evaluated search queries, intent, and information retrieval for AI training. I maintained high accuracy while processing complex datasets to assess relevance, intent, and high-volume workflows. The experience prioritized tool use correctness and informational accuracy under tight deadlines. • Labeled and assessed search relevance and intent for AI tools • Maintained high metric standards for complex annotation tasks • Ensured data reliability and accuracy in annotation workflows • Processed large batches of data under high-pressure conditions

As a Data Annotator at OneForma, I evaluated search queries, intent, and information retrieval for AI training. I maintained high accuracy while processing complex datasets to assess relevance, intent, and high-volume workflows. The experience prioritized tool use correctness and informational accuracy under tight deadlines. • Labeled and assessed search relevance and intent for AI tools • Maintained high metric standards for complex annotation tasks • Ensured data reliability and accuracy in annotation workflows • Processed large batches of data under high-pressure conditions

2023 - Present

Quality Analyst — AI Training & Data Assurance

AppenText

As a Quality Analyst at Appen, I performed audits, regression testing, and guideline optimizations on large-scale AI datasets. My tasks included tracking model drift, enforcing safety compliance, and refining annotation metrics. The focus was on improving factual consistency and bias/fairness standards in AI outputs. • Conducted detailed quality audits on AI datasets • Identified and addressed data/model quality issues over time • Ensured adherence to NSFW and fairness/bias guidelines • Improved annotation clarity and evaluation metrics

As a Quality Analyst at Appen, I performed audits, regression testing, and guideline optimizations on large-scale AI datasets. My tasks included tracking model drift, enforcing safety compliance, and refining annotation metrics. The focus was on improving factual consistency and bias/fairness standards in AI outputs. • Conducted detailed quality audits on AI datasets • Identified and addressed data/model quality issues over time • Ensured adherence to NSFW and fairness/bias guidelines • Improved annotation clarity and evaluation metrics

2023 - Present

Dataset Development for AI Systems (Aqua Projects)

OtherTextData Collection

For AI system dataset development (Aqua Projects), I labeled and validated machine learning (ML) datasets while ensuring thorough data cleaning. I focused on removing erroneous or misleading data points to enhance the quality of ML inputs. The process emphasized the impact of rigorous data preparation to improve downstream model performance. • Labeled structured and unstructured data for ML applications • Cleaned and validated data to reduce error propagation • Supported training set preparation for multiple ML domains • Performed quality assurance for input/output data flows

For AI system dataset development (Aqua Projects), I labeled and validated machine learning (ML) datasets while ensuring thorough data cleaning. I focused on removing erroneous or misleading data points to enhance the quality of ML inputs. The process emphasized the impact of rigorous data preparation to improve downstream model performance. • Labeled structured and unstructured data for ML applications • Cleaned and validated data to reduce error propagation • Supported training set preparation for multiple ML domains • Performed quality assurance for input/output data flows

2024 - 2024

Education

Work History