Elder K.

Key Skills

Software

Labelbox

Top Subject Matter

LLM dataset validation and data quality engineering

Legal Services & Contract Review

Regulatory Compliance & Risk Analysis

Top Data Types

Text

Document

Top Task Types

Classification

Tracking

Freelancer Overview

AI Data & Evaluation Specialist (LLM evaluation & dataset validation) - Turing (Remote). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox, JSON parsing, and Git. Education includes Bachelor of Science, University of Lagos (Unilag) (2022) and Professional Certificate (Coursera), Google Advanced Data Analytics Professional Certificate – Coursera (2023). AI-training focus includes data types such as Text and Document and labeling workflows including Classification and Tracking.

Labeling Experience

AI Data & Evaluation Specialist - Turing

LabelboxDocumentTracking

Review, grade, and refine AI model outputs for enterprise clients, validating technical logic, factual accuracy, and code-generation samples across multiple frameworks. Identify, document, and patch edge-case logical failures and model hallucinations by writing definitive "golden responses" to re-train neural networks. Deconstruct multi-page annotation style guides to ensure 100% compliance across massive data-labeling batches, achieving a consistent 99.4% quality assurance rating. Author clear, highly structured markdown justifications and source citations explaining why specific model responses were penalized or approved.

2025 - Present

AI Data & Evaluation Specialist (LLM evaluation & dataset validation) - Turing (Remote)

LabelboxTextClassification

Evaluating and grading AI model outputs; ensuring compliance with annotation style guides; validating factual accuracy and logic; determining whether responses are approved or penalized.

2025 - Present

Data Analyst (Data Infrastructure) - Awarri

LabelboxTextTracking

Managed the data pipeline cleaning and preprocessing stages for localized AI models, auditing unstructured text, documentation, and metadata for semantic consistency. Collaborated directly with machine learning engineering teams to construct structured JSON-based labeling taxonomies, reducing downstream model categorization errors by 18%. Flagged, triaged, and documented ambiguous text samples and out-of-scope data anomalies using internal Jira tracking systems, providing explicit root-cause write-ups. Consistently exceeded weekly data validation and review quotas by 15% while operating independently under light supervisory oversight.

2022 - 2024

Education

Work History