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Gilbert Obiekwe

Gilbert Obiekwe

AI Trainer - Annotation, Labelling, LLMs, & Agentic evaluation and Quality Assessment

NIGERIA flag
Lagos, Nigeria
$15.00/hrExpertOther

Key Skills

Software

Other

Top Subject Matter

No subject matter listed

Top Data Types

AudioAudio
DocumentDocument
ImageImage
TextText
VideoVideo

Top Label Types

Object Detection
Action Recognition
RLHF
Fine Tuning
Evaluation Rating

Freelancer Overview

I am an experienced AI trainer and data annotator with over 7 years working on diverse projects involving text, image, audio, and video datasets. My expertise lies in data labeling, model evaluation, and quality assurance to help train and refine AI and machine learning models for accuracy, fairness, and contextual understanding. I have hands-on experience using a wide range of tools including HubSpot, Intercom, Zendesk, Jira, Trello, and advanced AI-powered platforms. My background spans B2B SaaS, e-commerce, and customer support domains, where I have consistently ensured high-quality data annotation, maintained data integrity, and provided actionable feedback for model improvement. I am skilled at working independently as part of global teams, leveraging my technical knowledge in HTML/CSS, JavaScript, SQL, and AI prompt generation to streamline workflows and deliver reliable training data for cutting-edge AI solutions.

ExpertEnglish

Labeling Experience

Image Annotation

Don T DiscloseImageObject DetectionAction Recognition
Faceswap Capability Here, we are looking at the following questions while evaluating: Are the identities of the subject in the Face Image preserved in the Target Image? Are there artifacts, limb disfiguration, and/or other logical issues in the images (either Source or Output)? Logical issues: If the source and image are photorealistic, are they looking realistic and having no factual errors? e.g., having a woman's face on a man’s body, skin tone does not match between face and body, the hair does not match Are the expressions in the Source Image and Target Image the same? Expression also includes: smile (showing teeth vs not), direction of eye gaze, eyebrows, and head tilt. Evaluation Instructions: If any of the images contain any of the errors above, select “fail” and choose the failure reason. Otherwise, select “pass”. IMPORTANT: The faceswap should affect exactly one person - the person within the red bounding box in Source Image A If 0 people or more than 1 person are changed

Faceswap Capability Here, we are looking at the following questions while evaluating: Are the identities of the subject in the Face Image preserved in the Target Image? Are there artifacts, limb disfiguration, and/or other logical issues in the images (either Source or Output)? Logical issues: If the source and image are photorealistic, are they looking realistic and having no factual errors? e.g., having a woman's face on a man’s body, skin tone does not match between face and body, the hair does not match Are the expressions in the Source Image and Target Image the same? Expression also includes: smile (showing teeth vs not), direction of eye gaze, eyebrows, and head tilt. Evaluation Instructions: If any of the images contain any of the errors above, select “fail” and choose the failure reason. Otherwise, select “pass”. IMPORTANT: The faceswap should affect exactly one person - the person within the red bounding box in Source Image A If 0 people or more than 1 person are changed

2023 - 2023

Education

H

Heriot-Watt University

Bachelor of Engineering, Mechanical Engineering

Bachelor of Engineering
2017 - 2018
F

Federal Polytechnic Mubi

Higher National Diploma, Mechanical Engineering

Higher National Diploma
2004 - 2008

Work History

I

Invisible Technologies USA

Advanced Artificial Intelligence Data Trainer

San Francisco
2023 - Present
I

Invisible Technologies

Customer Experience Agent

Lagos
2021 - Present