Mutuma Gitobu

AI Data Trainer and Annotation Specialist with advanced expertise in data labeling, reviewing, quality assurance, and AI model evaluation across text, image, and cybersecurity datasets. Proven ability to deliver high-accuracy annotations, optimize dataset quality, and improve model performance through rigorous QA, error analysis, and guideline adherence. Experienced in Generative AI evaluation, including hallucination detection, bias identification, and prompt-response validation.

Strong background in cybersecurity data annotation, including threat detection and log analysis. Also interested in contributing as a Subject Matter Expert (SME) on high-impact AI and Cybersecurity training projects.

AI Data Annotation Specialist / Data Trainer at iMerit (2025-01 – Present)

• Executed high-volume, high-accuracy annotation across multimodal datasets (text, image, video)
• Evaluated Generative AI outputs for factual accuracy, bias, safety, and hallucinations
• Applied annotation guidelines to ensure dataset consistency and scalability
• Performed QA reviews and validation to improve dataset integrity and reduce errors
• Resolved edge cases and ambiguous data scenarios using analytical judgment
• Optimized datasets to enhance downstream AI model performance
• Maintained ≥95% annotation accuracy across multiple large-scale AI projects
• Increased QA pass rates through structured feedback loops and error analysis
• Reduced annotation inconsistencies by standardizing guideline interpretations

Image Reviewer at iMerit (2026-01 – Present)

• Reviewed annotated image datasets for accuracy, completeness, and compliance
• Validated object detection, classification, and segmentation outputs
• Applied quality standards to ensure high-confidence training data
• Identified labeling inconsistencies and provided corrective feedback
• Conducted dataset audits to maintain annotation integrity
• Collaborated with annotation teams to improve visual data quality
• Improved image dataset accuracy through detailed review processes
• Reduced visual annotation errors via structured feedback mechanisms
• Enhanced model training quality by ensuring high-fidelity labeled data

House Chores Documentation Assistant at Micro1 (2026-01 – Present)

• Recorded short, structured videos of everyday household tasks for AI and robotics model training
• Demonstrated natural execution of daily activities including cleaning, laundry, floor care, and food preparation
• Used smartphone with 0.5x wide-angle camera mounted on a headband for hands-free recording
• Followed detailed platform instructions for framing, duration, task flow, and environment setup
• Ensured recording areas were clean, uncluttered, and properly lit before filming
• Uploaded video files reliably using home internet connection while meeting submission deadlines
• Maintained consistency across recordings to support high-quality machine-learning datasets
• Delivered high-quality egocentric video datasets with ≥98% compliance to platform guidelines, improving usability for AI and robotics training models
• Increased dataset consistency and annotation readiness by standardizing task execution and recording environments, reducing preprocessing and rejection rates
• Achieved on-time submission rate of 100% across multiple tasks, ensuring uninterrupted data pipeline flow for machine learning operations

Cybersecurity Data Annotation at Remote (2024-01 – Present)

• Analyzed security logs and threat intelligence data for anomaly detection
• Performed cybersecurity data annotation for AI-driven detection systems
• Identified malicious vs normal behavior patterns in datasets
• Conducted threat analysis and vulnerability assessments
• Supported incident response and forensic investigations
• Documented findings and contributed to security reporting
• Enhanced threat detection datasets through accurate labeling
• Improved anomaly detection models via high-quality annotations
• Strengthened cybersecurity insights through structured data analysis

Master of Science in Information Technology Security Audit – Kabarak University (2028-01)