For employers

Hire this AI Trainer

Adediji

Agency

Kwara, Nigeria

piuscity.com

Key Skills

Software

Appen

Telus

OneForma

Mindrift

Data Annotation Tech

Labelbox

LabelImg

Other

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Task Types

Bounding Box

Text Generation

Object Detection

Evaluation/Rating

Prompt + Response Writing (SFT)

Company Overview

Pius_City Freelance Hub is an AI workforce solutions company focused on delivering reliable, scalable, and high-quality data annotation, AI training, and search evaluation services for artificial intelligence and machine learning projects. Our mission is to build a skilled African digital workforce capable of supporting global AI development through accuracy, efficiency, and professional project execution. We specialize in search engine evaluation, AI data training, audio annotation, video annotation, text and image labeling, prompt evaluation, dataset validation, quality assurance, and RLHF-related tasks. Our workflow combines structured quality control systems, reviewer-based validation, and remote team coordination to maintain consistency and accuracy across projects. The company currently operates with a trained remote workforce of over 50 AI data specialists experienced in handling both short-term and large-scale annotation operations. Our team has practical experience working on international AI projects involving multilingual datasets, search relevance evaluation, speech annotation, and multimodal AI training. Pius_City Freelance Hub was established to bridge the gap between global AI companies and highly trainable African talent by providing dependable outsourcing support for AI operations. We operate remotely and support clients across different regions, including the United States, United Kingdom, Canada, and Africa. Our priorities include data confidentiality, operational reliability, timely delivery, and long-term professional collaboration. Our growing experience with international AI workflows and evaluation standards positions us as a dependable workforce partner for organizations seeking scalable human-in-the-loop support systems.

Security

Security Overview

Pius_City Freelance Hub is committed to maintaining strong operational security, data confidentiality, and responsible workforce management across all AI data and annotation projects. Our company follows controlled workflow practices designed to protect client information, project materials, and operational processes. Access to project files and sensitive data is restricted only to authorized team members assigned to specific tasks. Team members are instructed on confidentiality expectations, responsible data handling, and secure communication practices before participating in any project. We operate through structured remote workforce management systems that help monitor productivity, maintain accountability, and reduce unauthorized data exposure. Internal quality review processes are implemented to ensure project accuracy while maintaining compliance with client guidelines and confidentiality requirements. To support secure project execution, we encourage the use of: • Password-protected work systems • Two-factor authentication where applicable • Secure internet connections • Restricted sharing of client materials • Controlled access to project platforms and tools Our team members are trained to avoid unauthorized duplication, distribution, or external use of client data and project resources. Communication related to projects is managed through approved channels to maintain professionalism and information security. Pius_City Freelance Hub also prioritizes operational reliability by maintaining organized task management systems, reviewer-based quality control, and supervision structures for large-scale annotation projects. These systems help ensure consistent performance, timely delivery, and secure coordination among remote staff members. As we continue to grow, we remain committed to strengthening our security practices and maintaining professional standards that support long-term partnerships with AI companies, technology organizations, and data-driven businesses globally.

Labeling Experience

Audio Annotator

Other

Audio

Segmentation

This project involved large-scale audio annotation and speech data processing for AI training and language technology improvement. The scope of the project included audio segmentation, speech labeling, speaker identification, transcription review, audio classification, timestamp validation, and quality assurance for multilingual speech datasets. The project was executed remotely in collaboration with DataForce and supported machine learning models focused on speech recognition, natural language processing, and voice-based AI systems. Our team handled structured annotation workflows using client-provided guidelines and platform tools to ensure consistency and accuracy across all assigned tasks. A team of 15 trained annotators worked on the project, managing high-volume audio datasets within strict turnaround timelines. Quality control measures included multi-level review processes, guideline compliance checks, reviewer validation, and regular performance monitoring to maintain annotation accuracy and project reliability. The project required strong attention to detail, confidentiality, workflow coordination, and adherence to client quality standards throughout the annotation lifecycle.

2025 - 2025

Ai Data Trainer

Appen

Text

Text Generation

Question Answering

This project involved AI data training and human feedback operations designed to improve the performance, accuracy, and relevance of machine learning models. The scope of the project included prompt evaluation, response rating, search relevance assessment, content categorization, data validation, and quality review tasks used in training large AI systems. The project supported the development of artificial intelligence models by providing structured human feedback and high-quality labeled datasets required for model optimization and performance improvement. Team members worked with detailed project guidelines to evaluate content accuracy, relevance, user intent alignment, and language quality across multiple data categories. A trained remote team was assigned to manage task execution, workflow coordination, and quality assurance throughout the project lifecycle. The operation involved handling large volumes of AI training data while maintaining consistency, confidentiality, and timely delivery. Quality measures implemented during the project included reviewer-based validation, multi-level quality checks, guideline compliance monitoring, accuracy scoring, and continuous feedback processes to ensure high annotation standards. The project required strong analytical skills, attention to detail, communication, and the ability to follow complex AI evaluation standards within a fast-paced remote work environment.

2023 - 2025

Social Media Evaluator

Appen

Image

Evaluation/Rating

This project involved social media evaluation and content assessment tasks aimed at improving the quality, relevance, safety, and user experience of social media platforms and AI-powered recommendation systems. The scope of the project included evaluating social media content relevance, reviewing user-generated posts, analyzing engagement quality, assessing advertisement accuracy, identifying policy violations, and rating content based on platform guidelines and user intent. The project supported the improvement of content recommendation algorithms, moderation systems, and AI-driven user experience models through structured human feedback and detailed content analysis. Evaluators were responsible for reviewing various forms of digital content, including text, images, videos, and advertisements, while ensuring compliance with established evaluation standards. A trained remote team managed daily evaluation tasks and maintained workflow efficiency, confidentiality, and consistent performance throughout the project lifecycle. The project involved handling large volumes of content across multiple categories within strict quality and turnaround expectations. Quality assurance measures included guideline compliance reviews, reviewer validation processes, accuracy monitoring, feedback implementation, and multi-level quality checks to maintain high evaluation standards. The project required strong analytical skills, attention to detail, decision-making ability, and a solid understanding of online content standards, user behavior, and digital platform policies in a fast-paced remote work environment.

2022 - 2024

Search Engine Evaluator

Other

Text

Text Generation

RLHF

This project involved search engine evaluation and relevance assessment tasks designed to improve the accuracy, usefulness, and overall quality of search engine results and AI-driven recommendation systems. The scope of the project included evaluating search result relevance, analyzing user intent, rating webpage quality, assessing content usefulness, validating query-result relationships, and reviewing localized search outputs based on detailed client guidelines. The project supported the optimization of search algorithms and machine learning systems by providing structured human feedback on search accuracy, ranking effectiveness, and content relevance. Evaluators were responsible for analyzing search queries and determining how well returned results satisfied user intent across different categories and topics. A trained remote team managed daily evaluation workflows while maintaining productivity, consistency, and confidentiality throughout the project lifecycle. The project involved handling high volumes of evaluation tasks within strict quality and turnaround expectations. Quality assurance measures included reviewer validation, guideline compliance checks, accuracy monitoring, feedback-based corrections, and multi-level quality review processes to ensure reliable evaluation outcomes. The project required strong analytical thinking, internet research skills, attention to detail, and the ability to apply complex rating standards consistently in a remote work environment.

2021 - 2023