Ian N.

Key Skills

Software

Appen

Scale AI

Roboflow

Labelbox

Top Subject Matter

Computer Vision (Object Detection, Segmentation)

Computer Vision (Video Tracking)

Audio AI (Speech Recognition, Diarization)

Top Data Types

Image

Video

Audio

Text

Top Task Types

Object Detection

Tracking

Transcription

Entity (NER) Classification

Freelancer Overview

AI Training Specialist – Data Labelling & Annotation (Freelance) | Appen, Scale AI, Microworkers & Others. Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Appen, Scale AI, and Microworkers & Others. Education includes Bachelor of Science, South Eastern Kenya University (2025) and Kenya Certificate of Secondary Education, Kiaguthu Boys High School (2021). AI-training focus includes data types such as Image, Video, and Audio and labeling workflows including Object Detection, Tracking, and Transcription.

Labeling Experience

Translator & Linguist – AI Language Training (Freelance) | Microworkers, Go Transcript, Uber AI

Text

Provided English, Swahili, and Kikuyu translation and localization for large-scale AI training datasets. Conducted semantic analysis to ensure technical terminology was adapted accurately for local speakers while preserving meaning for model training. Managed labeling workflows with strict quality and privacy compliance across multiple platforms. • English/Swahili/Kikuyu localization • Semantic analysis for technical term adaptation • Compliance-focused translation workflow management • Cross-team collaboration on translation protocols and output standards

2024 - Present

AI Training Specialist – Text/NLP Annotation (Freelance) | Appen, Scale AI, Microworkers & Others

RoboflowTextEntity (NER) Classification

Performed text and NLP labeling tasks including sentiment analysis, intent classification, named entity recognition, and dialogue annotation. Applied structured labeling approaches to produce high-quality training data for natural language processing models. Reviewed and quality-checked outputs to maintain dataset accuracy, consistency, and guideline compliance. • Sentiment analysis labeling • Intent classification • Named entity recognition (NER) and dialogue annotation • Quality checks for inter-annotator consistency

2023 - Present

AI Training Specialist – Audio Labelling & Transcription (Freelance) | Appen, Scale AI, Microworkers & Others

AudioTranscription

Labeled and transcribed audio datasets for speech recognition and audio AI models. Used ELAN and Audino to annotate speech segments, perform speaker diarization, and capture environmental sounds. Ensured audio label consistency and compliance with client privacy and quality guidelines for training-ready outputs. • Speech segment annotation • Speaker diarization • Environmental sound labeling • Audio transcription with ELAN/Audino workflows

2023 - Present

AI Training Specialist – Video Annotation & Multi-Object Tracking (Freelance) | Appen, Scale AI, Microworkers & Others

AppenVideoTracking

Annotated video datasets for multi-object tracking by labeling moving objects frame-by-frame with consistent ID tracking across sequences. Applied tracking-oriented quality practices to ensure temporal consistency and reliable annotations for motion-focused computer vision models. Followed production and platform benchmarks while completing labeling work under varying client guidelines. • Multi-object tracking frame-by-frame labeling • Consistent ID assignment across video sequences • Tracking-focused quality assurance • Dataset preparation support for computer vision models

2023 - Present

AI Training Specialist – Data Labelling & Annotation (Freelance) | Appen, Scale AI, Microworkers & Others

AppenImageObject Detection

Performed high-volume image annotation for computer vision training, including bounding boxes, polygon-based delineation, semantic segmentation, and key point labeling. Built and validated object detection datasets using YOLO-oriented workflows, ensuring class labeling and detection output checks. Maintained strict adherence to client guidelines, ontologies, and inter-annotator agreement requirements to minimize rework. • Bounded box, polygon, semantic segmentation, and key point annotation • YOLO class labeling and validation • Quality checking of peer annotations for consistency • Tooling and dataset management for production-ready exports

2023 - Present

Education

Work History