Mwendwa Mulili

Key Skills

Software

AWS SageMaker

Appen

Axiom AI

Clickworker

CloudFactory

CrowdFlower

CrowdSource

Data Annotation Tech

Dataloop

Deep Systems

Encord

Google Cloud Vertex AI

Labelbox

LabelImg

Label Studio

Mercor

Mindrift

OneForma

OpenCV AI Kit (OAK)

Prodigy

Remotasks

Roboflow

Surge AI

Toloka

Telus

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Audio

Computer Code Programming

Document

Geospatial Tiled Imagery

Image

Text

Top Label Types

Action Recognition

Audio Recording

Bounding Box

Classification

Computer Programming Coding

Cuboid

Data Collection

Emotion Recognition

Evaluation Rating

Mapping

Object Detection

Point Key Point

Polygon

Polyline

Prompt Response Writing SFT

Question Answering

Red Teaming

RLHF

Routing

Segmentation

Text Generation

Text Summarization

Tracking

Transcription

Translation Localization

Freelancer Overview

I am an experienced AI trainer and data annotator with a strong background in labeling and validating text, image, and audio datasets for NLP and computer vision projects. My expertise includes using tools like Label Studio, CVAT, and Excel/Google Sheets to annotate data, perform QA, and ensure high-quality training sets for large language models. I have contributed to AI evaluation tasks on platforms such as Outlier AI, Prolific, and Upwork, where I reviewed AI-generated responses, flagged inconsistencies, and followed structured guidelines to maintain data integrity. My technical skills in Python, JavaScript/TypeScript, React, Node.js, and database management allow me to efficiently handle structured data, automate workflows, and support full-stack web applications. I am highly detail-oriented, organized, and adept at managing multiple projects, with hands-on experience in scenario design, dataset validation, and multilingual transcription for diverse domains including e-commerce and research.

Labeling Experience

AI Data Annotation & Model Training, Full-Stack Web Applications

Label StudioTextClassification

Annotated large-scale text and image datasets for classification, sentiment analysis, summarization, and defect detection. Ensured quality by thorough data entry, validation, and structured QA processes. Reviewed AI outputs and applied corrective protocols to maintain data quality standards. • Used Label Studio and Excel/Google Sheets for managing annotation tasks • Conducted manual and automated validation of annotated data • Collaborated on full-stack web app development relevant to annotation workflows • Maintained versioned records with Git/GitHub and Postman

Annotated large-scale text and image datasets for classification, sentiment analysis, summarization, and defect detection. Ensured quality by thorough data entry, validation, and structured QA processes. Reviewed AI outputs and applied corrective protocols to maintain data quality standards. • Used Label Studio and Excel/Google Sheets for managing annotation tasks • Conducted manual and automated validation of annotated data • Collaborated on full-stack web app development relevant to annotation workflows • Maintained versioned records with Git/GitHub and Postman

2025 - 2025

AI Trainer / Data Annotator – Outlier AI

Label StudioTextEvaluation Rating

Reviewed and validated AI-generated question-and-answer pairs for accuracy and logical consistency. Applied structured evaluation guidelines to maintain dataset quality. Flagged ambiguous or non-answerable content during annotation processes. • Performed quality assurance and correction of labeled AI data • Used structured feedback to continually improve dataset accuracy • Maintained high annotation quality scores per project standards • Tools included Python, JSON/YAML, and Excel/Google Sheets

Reviewed and validated AI-generated question-and-answer pairs for accuracy and logical consistency. Applied structured evaluation guidelines to maintain dataset quality. Flagged ambiguous or non-answerable content during annotation processes. • Performed quality assurance and correction of labeled AI data • Used structured feedback to continually improve dataset accuracy • Maintained high annotation quality scores per project standards • Tools included Python, JSON/YAML, and Excel/Google Sheets

2025 - 2025

Research Participant & AI Task Contributor – Prolific

Label StudioTextEvaluation Rating

Conducted structured text and image data review for AI research and validation tasks. Provided detailed feedback for dataset validation and model training research. Followed strict annotation instructions supporting scientific research standards. • Participated in both annotation and evaluation projects • Supported dataset preparation for AI research purposes • Used Label Studio and Python for annotation and review • Maintained adherence to project protocols and timelines

Conducted structured text and image data review for AI research and validation tasks. Provided detailed feedback for dataset validation and model training research. Followed strict annotation instructions supporting scientific research standards. • Participated in both annotation and evaluation projects • Supported dataset preparation for AI research purposes • Used Label Studio and Python for annotation and review • Maintained adherence to project protocols and timelines

2024 - 2025

AI Evaluation & Scenario Design Exercises – Personal Projects

LabelboxTextEvaluation Rating

Designed and executed mock LLM evaluation scenarios with structured formats. Defined golden paths, expected outputs, and edge cases for model evaluation. Simulated agent responses and applied scoring logic for reproducible AI evaluation workflows. • Developed detailed scenario-based evaluation templates • Supported research on LLM behavior and output consistency • Used Python, JSON, and YAML for structured scenario definition • Managed version control with Git/GitHub in project workflows

Designed and executed mock LLM evaluation scenarios with structured formats. Defined golden paths, expected outputs, and edge cases for model evaluation. Simulated agent responses and applied scoring logic for reproducible AI evaluation workflows. • Developed detailed scenario-based evaluation templates • Supported research on LLM behavior and output consistency • Used Python, JSON, and YAML for structured scenario definition • Managed version control with Git/GitHub in project workflows

2023 - 2024

AI Trainer / Data Annotator – Upwork & AI Training Platforms

Label StudioTextClassification

Trained and evaluated large language models (LLMs) through high-quality NLP data annotation. Performed tasks such as classification, summarization, and sentiment analysis. Reviewed and improved AI-generated content for quality assurance. • Maintained annotation consistency across large datasets • Applied client-specific quality guidelines to outputs • Managed text-based QA processes and validations • Leveraged Label Studio, Python, and Excel for annotation workflows

Trained and evaluated large language models (LLMs) through high-quality NLP data annotation. Performed tasks such as classification, summarization, and sentiment analysis. Reviewed and improved AI-generated content for quality assurance. • Maintained annotation consistency across large datasets • Applied client-specific quality guidelines to outputs • Managed text-based QA processes and validations • Leveraged Label Studio, Python, and Excel for annotation workflows

2023 - 2024

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Education

Work History