Albanus Maingi

Key Skills

Software

Argilla

Deep Systems

Appen

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Image

Audio

Text

Computer Code Programming

Top Label Types

Segmentation

Audio Recording

Data Collection

Computer Programming Coding

Freelancer Overview

I am a highly motivated and detail-oriented individual with hands-on experience in data annotation, having contributed to AI training data projects at Africa AI. My background in education has strengthened my communication, organizational, and teamwork skills, enabling me to follow complex guidelines and ensure high-quality, accurate labeling. I am adaptable, eager to learn, and committed to delivering reliable results on time. I am passionate about contributing to innovative AI solutions and thrive in collaborative environments where precision and consistency are valued.

Labeling Experience

africa ai

Internal Proprietary ToolingComputer Code ProgrammingComputer Programming Coding

Below is a precise, professional description of your scope in web scraping, aligned with your experience at Africa AI: Scope of Work – Web Scraping & Data Extraction As a Web Scraper / Data Specialist, my scope covered the full data acquisition and preprocessing pipeline to support AI and machine learning workflows: 1. Data Source Identification Identified relevant websites and structured/unstructured data sources aligned with project objectives. Assessed site structure (HTML, DOM hierarchy, pagination, APIs). 2. Data Extraction & Automation Developed and executed scraping scripts using Python-based tools (e.g., BeautifulSoup). Extracted large-scale structured data including text, metadata, product listings, and tabular datasets. Handled pagination, dynamic content, and basic anti-bot limitations. 3. Data Cleaning & Transformation Removed duplicates, incomplete entries, and noisy data. Standardized formats (dates, currencies, categorical labels). Converted raw scraped data

Below is a precise, professional description of your scope in web scraping, aligned with your experience at Africa AI: Scope of Work – Web Scraping & Data Extraction As a Web Scraper / Data Specialist, my scope covered the full data acquisition and preprocessing pipeline to support AI and machine learning workflows: 1. Data Source Identification Identified relevant websites and structured/unstructured data sources aligned with project objectives. Assessed site structure (HTML, DOM hierarchy, pagination, APIs). 2. Data Extraction & Automation Developed and executed scraping scripts using Python-based tools (e.g., BeautifulSoup). Extracted large-scale structured data including text, metadata, product listings, and tabular datasets. Handled pagination, dynamic content, and basic anti-bot limitations. 3. Data Cleaning & Transformation Removed duplicates, incomplete entries, and noisy data. Standardized formats (dates, currencies, categorical labels). Converted raw scraped data

2025 - 2025

AI Trainer / LLM Contributor – Silencio

Deep SystemsAudioAudio Recording

As an AI Trainer and LLM Contributor at Silencio, I reviewed and rated AI-generated responses for model improvement. I provided structured feedback for reinforcement learning workflows (RLHF). I also assisted in prompt evaluation and ensured outputs met compliance standards. • Rated and evaluated large volumes of AI outputs. • Provided direct feedback for model fine-tuning iterations. • Helped develop and maintain quality guidelines for the annotation team. • Participated in linguistic and policy compliance reviews.

As an AI Trainer and LLM Contributor at Silencio, I reviewed and rated AI-generated responses for model improvement. I provided structured feedback for reinforcement learning workflows (RLHF). I also assisted in prompt evaluation and ensured outputs met compliance standards. • Rated and evaluated large volumes of AI outputs. • Provided direct feedback for model fine-tuning iterations. • Helped develop and maintain quality guidelines for the annotation team. • Participated in linguistic and policy compliance reviews.

2025 - 2025

Data Annotator – CrowdGen

ArgillaImageSegmentation

As a Data Annotator at CrowdGen, I labeled and annotated large datasets to support AI model training pipelines. My tasks involved sentiment analysis, entity tagging, and intent classification on various text data. I also conducted quality control checks to uphold annotation standards. • Performed structured labeling for sentiment, entities, and intent. • Consistently delivered high accuracy work and met benchmarks. • Participated in LLM output evaluation for factual accuracy and relevance. • Utilized data annotation tools and followed comprehensive guidelines.

As a Data Annotator at CrowdGen, I labeled and annotated large datasets to support AI model training pipelines. My tasks involved sentiment analysis, entity tagging, and intent classification on various text data. I also conducted quality control checks to uphold annotation standards. • Performed structured labeling for sentiment, entities, and intent. • Consistently delivered high accuracy work and met benchmarks. • Participated in LLM output evaluation for factual accuracy and relevance. • Utilized data annotation tools and followed comprehensive guidelines.

2025 - 2025

LLM & AIGC Project Contributor – Appen

AppenTextData Collection

As a contributor on LLM & AIGC projects at Appen, I participated in large-scale AI data collection and annotation efforts. My primary task was labeling conversational datasets for NLP and chatbot training. I regularly evaluated AI output for contextual and linguistic accuracy. • Collected and annotated conversational training data. • Labeled dialogue and contextual datasets for NLP applications. • Ensured strict adherence to confidentiality practices. • Conducted output validation and reported errors for remediation.

As a contributor on LLM & AIGC projects at Appen, I participated in large-scale AI data collection and annotation efforts. My primary task was labeling conversational datasets for NLP and chatbot training. I regularly evaluated AI output for contextual and linguistic accuracy. • Collected and annotated conversational training data. • Labeled dialogue and contextual datasets for NLP applications. • Ensured strict adherence to confidentiality practices. • Conducted output validation and reported errors for remediation.

2024 - 2024

Education

Work History