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Solomon Uche

Solomon Uche

AI Trainer and Data Annotator - Machine Learning

NIGERIA flag
Akute, Nigeria
$20.00/hrExpertHivemindImeritLabelbox

Key Skills

Software

HiveMindHiveMind
iMeritiMerit
LabelboxLabelbox
ProdigyProdigy
TelusTelus
Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

VideoVideo
ImageImage
AudioAudio
TextText

Top Label Types

Action Recognition
Entity Ner Classification
Emotion Recognition
Evaluation Rating
Segmentation
Relationship
Classification
Data Collection
Audio Recording

Freelancer Overview

I am a detail-oriented AI Trainer and Data Annotator with hands-on experience labeling, structuring, and validating diverse datasets for machine learning and AI model development. My background spans image, text, audio, and video annotation for projects in computer vision, natural language processing, and speech recognition, supporting domains such as autonomous systems and healthcare. I excel at following complex guidelines, conducting thorough quality assurance reviews, and collaborating remotely with cross-functional teams to optimize workflows and improve data quality. Skilled in data entry, content review, and transcription, I consistently exceed accuracy benchmarks and deliver high-quality results under tight deadlines, using tools like Excel, Slack, and Google Workspace to manage large-scale annotation and data projects efficiently.

ExpertEnglish

Labeling Experience

AI Research Study- YPAI team

Internal Proprietary ToolingAudioData CollectionAudio Recording
We're building an AI to detect deepfakes using real human recordings. Your participation provides crucial data that can't be generated artificially, helping create more reliable video authentication tools. What participation involves: A 1 hour 45 minute - 2 hour group session, where we’ll have a natural chat and answer simple questions about everyday topics, just like talking with friends.

We're building an AI to detect deepfakes using real human recordings. Your participation provides crucial data that can't be generated artificially, helping create more reliable video authentication tools. What participation involves: A 1 hour 45 minute - 2 hour group session, where we’ll have a natural chat and answer simple questions about everyday topics, just like talking with friends.

2025 - 2025

English conversation recording project- MLG international

Internal Proprietary ToolingAudioAudio Recording
The task involves being recorded in English conversations covering a variety of everyday topics.

The task involves being recorded in English conversations covering a variety of everyday topics.

2025

Atomic Action project- Atlas

Internal Proprietary ToolingVideoAction Recognition
This project facilitates a human egocentric video annotation workflow. You will be annotating videos showing humans completing physical tasks from a first-person (ego) perspective.

This project facilitates a human egocentric video annotation workflow. You will be annotating videos showing humans completing physical tasks from a first-person (ego) perspective.

2025

Shopping Assistant Conversation Evaluation- Innodata india

Internal Proprietary ToolingTextSegmentationRelationship
The agent is designed to assist the user in locating an apparel item for purchase, such as a shirt, sneakers, etc. The user and the agent may go back and forth several times. The task is to carefully review the conversation and evaluate three metrics related to user satisfaction.

The agent is designed to assist the user in locating an apparel item for purchase, such as a shirt, sneakers, etc. The user and the agent may go back and forth several times. The task is to carefully review the conversation and evaluate three metrics related to user satisfaction.

2025 - 2025

English Emotion Project-Babel audio

Internal Proprietary ToolingAudioEmotion RecognitionEvaluation Rating
EE2T is an advanced speech-annotation project focused on tagging emotions, vocal styles, and non-speech sounds in recorded dialogue. I ensured transcript accuracy, applied precise style and sound tags, and followed strict QA standards to help train high-quality, emotionally aware speech AI models.

EE2T is an advanced speech-annotation project focused on tagging emotions, vocal styles, and non-speech sounds in recorded dialogue. I ensured transcript accuracy, applied precise style and sound tags, and followed strict QA standards to help train high-quality, emotionally aware speech AI models.

2025 - 2025

Education

A

ALX

Honors, Professional Foundations

Honors
2024 - 2024
U

University of Benin

Bachelor's Degree, Biochemistry

Bachelor's Degree
2019 - 2023

Work History

B

Babel Audio

Audio Transcriptionist

Remote
2025 - 2025
U

Upwork

Virtual Assistant

Remote
2022 - 2025