Benneth C.

Key Skills

Software

Telus

Mercor

Appen

Top Subject Matter

LLM evaluation and AI output quality assurance

Supervised learning dataset annotation and QA

Dataset labeling for AI model training

Top Data Types

Text

Image

Document

Top Task Types

Classification

Transcription

Evaluation/Rating

Freelancer Overview

AI Training & Evaluation Specialist | Freelance – Remote. Core strengths include OpenTrain AI platform, N, and A. Education includes Bachelor of Science, Covenant University. AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

AI Training & Evaluation Specialist | Freelance – Remote

Text

Performed evaluation tasks on AI-generated text, code, and creative writing to assess accuracy, coherence, and user intent alignment. Delivered structured feedback reports that inform model training cycles and support measurable output improvements. Identified edge cases, hallucinations, and subtle anomalies with clear rationale for refinement and benchmarked model utility using simulated user scenarios. • Accuracy and coherence assessment for generated text and code outputs • User-intent alignment checks for realistic scenarios • Hallucination/edge-case detection with rationale • Structured feedback reporting for training iteration

2025 - Present

Data Annotation & QA Analyst | Mercore – Remote

Text

Annotated and labeled large-scale multimodal datasets, including text, images, and structured data, for supervised learning projects while following strict annotation guidelines. Conducted quality assurance reviews of digital content to produce actionable reports for refining output standards. Built reusable QA checklists and documentation to ensure consistent, reproducible annotation outcomes across high-volume batches. • Text, image, and structured-data labeling under guideline compliance • QA review of labeled content with actionable refinement notes • Development of QA checklists and annotation documentation • Ensuring label consistency and data integrity in high-volume batches

2025 - 2025

Data Annotation Specialist | Tellus International – Remote

TextClassification

Completed high-volume annotation tasks for model training with 98%+ inter-annotator agreement. Escalated ambiguous or low-quality samples to improve dataset reliability and maintain annotation quality. Contributed to internal annotation protocol improvements, reducing labeling errors and increasing team throughput. • High-volume dataset labeling for supervised model training • Inter-annotator agreement maintenance and consistency checks • Escalation workflow for ambiguous/low-quality data • Participation in protocol improvement to reduce labeling errors

2023 - 2025

Education

Work History