Adewunmi O.

Key Skills

Software

Other

Top Subject Matter

LLM response evaluation and text annotation (classification and NER)

Data QA for annotated datasets and taxonomy refinement

Top Data Types

Text

Image

Top Task Types

Classification

Freelancer Overview

AI Data Annotator & Training Specialist — Atlas Capture (Dec 2025 – Present). Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Bachelor of Arts, Olabisi Onabanjo University (2021). AI-training focus includes data types such as Text and labeling workflows including Entity (NER) and Classification.

Labeling Experience

AI Data Annotator & Training Specialist — Atlas Capture (Dec 2025 – Present)

OtherText

Execute end-to-end data annotation workflows for text classification and named entity recognition to produce high-quality training sets for machine learning pipelines. Evaluate, rank, and score LLM-generated responses using strict multidimensional matrices for helpfulness, truthfulness, logical consistency, and safety. Author gold-standard prompt-response pairs for supervised fine-tuning baseline data while providing written justifications for rejected outputs. • Perform edge-case auditing and adversarial testing (red-teaming) to isolate hallucinations. • Identify and document linguistic, factual, or logical errors in model outputs. • Support dataset integrity through analytical feedback and scoring.

2025 - Present

Data Specialist / Quality Assurance Analyst — AVA CAPITAL PLC (Oct 2024 – July 2025)

OtherTextClassification

Translate project guidelines into accurate structured labels for massive unstructured datasets. Collaborate with data scientists and machine learning engineers to refine annotation taxonomies and resolve labeling discrepancies. Maintain high quality assurance performance by meeting or exceeding target accuracy and speed baselines while preserving dataset integrity. • Perform manual correction workflows when automated labeling tools introduce systematic errors. • Analyze labeling outcomes to ensure adherence to guidelines. • Support consistent QA scoring at approximately 98.5% accuracy.

2024 - 2025

Education

Work History