Godwin I.

Key Skills

Software

AWS SageMaker

Appen

Clickworker

HiveMind

Top Subject Matter

AI response evaluation / QA for LLM outputs

Training data preparation

Qa Domain Expertise

Top Data Types

Text

Image

Medical Dicom

Top Task Types

Data Collection

Bounding Box

Classification

Entity (NER) Classification

Segmentation

Polygon

Freelancer Overview

AI Response Evaluator (Remote). Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Master of Science, University of Leeds. AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Data Collection.

Labeling Experience

AI Data Specialist (Present, Remote)

TextData Collection

Led data collection and preprocessing for AI model training to meet project-specific standards. Conducted thorough data evaluation and quality assurance to maintain consistency and accuracy across AI outputs. Annotated and labeled complex datasets aligned to machine learning requirements, then collaborated with product and engineering teams to troubleshoot and refine AI pipelines. • Collected and preprocessed data for model training. • Evaluated dataset quality through QA/validation checks. • Annotated and labeled datasets based on ML requirements. • Delivered progress reports with data quality improvements and model performance metrics.

Present

AI Response Evaluator (Remote)

Text

Evaluated AI-generated responses to assess clarity, safety, and accuracy against reliability benchmarks. Used internal QA tools and cross-functional feedback loops to improve data quality and downstream model behavior. Produced quality signals for refining AI pipelines and ensuring alignment with evaluation standards. • Assessed responses across diverse topics, flagging edge cases and inconsistencies. • Performed QA checks against clarity, safety, and accuracy criteria. • Partnered with cross-functional teams to enhance data pipelines feeding AI models. • Improved overall data reliability using evaluation insights.

Present

Education

Work History