Kipngeno Mutai

Key Skills

Software

Appen

CVAT

Labelbox

OneForma

Prodigy

SuperAnnotate

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Image

Text

Video

Top Label Types

Bounding Box

Classification

Data Collection

Emotion Recognition

Entity Ner Classification

Land Cover Classification

Object Detection

Point Key Point

Polygon

Polyline

Question Answering

RLHF

Segmentation

Text Generation

Text Summarization

Transcription

Translation Localization

Freelancer Overview

I am a certified Data Annotation Specialist with over 7 years of hands-on experience delivering high-quality training data for AI and machine learning projects across image, video, audio, and text modalities. My expertise spans computer vision, NLP, LLM evaluation, and multimodal annotation, with a proven record of 99%+ accuracy on 500+ projects using industry-standard tools like CVAT.ai, Labelbox, Prodigy, and Amazon SageMaker Ground Truth. I have contributed to robotics, autonomous vehicles, sentiment analysis, and ASR projects, ensuring data quality through rigorous QA, statistical validation, and continuous feedback integration. With a strong foundation in mathematics, computer science, and quality management, I am passionate about supporting ethical AI development and consistently delivering datasets that meet the highest industry standards.

Labeling Experience

Computer Vision Data Labeling Specialist: Bounding Box, Polygon & Segmentation Annotation

CVATImageBounding BoxPolygon

freelance experience delivering high-precision image and video annotation projects for global AI companies, autonomous vehicle startups, and computer vision research teams. Object Detection & Tracking: Executed detailed bounding box and polygon annotations for autonomous vehicle datasets, labeling pedestrians, vehicles, traffic signs, and cyclists across thousands of frames. Maintained temporal consistency in video tracking projects to ensure smooth object identification across sequences. Semantic Segmentation: Performed pixel-level segmentation for medical imaging datasets (organs, tumors) and retail shelf analysis, achieving 99%+ annotation accuracy through meticulous attention to boundary definition and occlusion handling. Quality Assurance: Implemented multi-stage validation workflows including cross-checking against gold-standard datasets and peer review cycles. Reduced error rates by 25% and consistently met or exceeded client specifications for production-rea

freelance experience delivering high-precision image and video annotation projects for global AI companies, autonomous vehicle startups, and computer vision research teams. Object Detection & Tracking: Executed detailed bounding box and polygon annotations for autonomous vehicle datasets, labeling pedestrians, vehicles, traffic signs, and cyclists across thousands of frames. Maintained temporal consistency in video tracking projects to ensure smooth object identification across sequences. Semantic Segmentation: Performed pixel-level segmentation for medical imaging datasets (organs, tumors) and retail shelf analysis, achieving 99%+ annotation accuracy through meticulous attention to boundary definition and occlusion handling. Quality Assurance: Implemented multi-stage validation workflows including cross-checking against gold-standard datasets and peer review cycles. Reduced error rates by 25% and consistently met or exceeded client specifications for production-rea

2024 - 2025

NLP Data Labeling Specialist: NER, Classification & Text Generation for LLM Training

LabelboxTextClassificationQuestion Answering

Project Description: Extensive experience delivering text annotation and NLP labeling projects for global AI research labs, LLM developers, and enterprise conversational AI teams using Labelbox. Entity Recognition & Classification: Performed fine-grained entity recognition (NER) for legal documents, medical records, and financial reports using Labelbox's ontology and relationship tools. Executed multi-label text classification for customer support tickets, news articles, and product reviews, ensuring consistent application of complex taxonomies across 50,000+ text samples. LLM Training & Alignment: Created high-quality prompt-response pairs for Supervised Fine-Tuning (SFT) of large language models. Participated in Reinforcement Learning from Human Feedback (RLHF) workflows within Labelbox, ranking model outputs based on helpfulness, harmlessness, and honesty to align AI behavior with human preferences.

Project Description: Extensive experience delivering text annotation and NLP labeling projects for global AI research labs, LLM developers, and enterprise conversational AI teams using Labelbox. Entity Recognition & Classification: Performed fine-grained entity recognition (NER) for legal documents, medical records, and financial reports using Labelbox's ontology and relationship tools. Executed multi-label text classification for customer support tickets, news articles, and product reviews, ensuring consistent application of complex taxonomies across 50,000+ text samples. LLM Training & Alignment: Created high-quality prompt-response pairs for Supervised Fine-Tuning (SFT) of large language models. Participated in Reinforcement Learning from Human Feedback (RLHF) workflows within Labelbox, ranking model outputs based on helpfulness, harmlessness, and honesty to align AI behavior with human preferences.

2024

Audio Transcription & Geospatial Data Labeling Specialist

AppenAudioEmotion RecognitionTranslation Localization

Comprehensive experience delivering audio and geospatial annotation projects for voice AI developers, media localization teams, navigation platforms, and autonomous systems using the Appen platform. Audio Transcription & Labeling: Delivered high-accuracy verbatim and edited transcriptions for podcast episodes, business meetings, academic lectures, and voice assistant training data through Appen's transcription interfaces. Performed speaker diarization and timestamp alignment for multi-speaker audio. Labeled emotion recognition datasets for affective computing and customer sentiment analysis. Translation & Localization: Supported multilingual AI training through translation validation and localization quality assessment within Appen's workflow. Ensured cultural appropriateness and contextual accuracy for global deployment of voice assistants and content platforms across English, Kiswahili, and other languages. Geospatial Annotation: Performed precise geocoding and land cover

Comprehensive experience delivering audio and geospatial annotation projects for voice AI developers, media localization teams, navigation platforms, and autonomous systems using the Appen platform. Audio Transcription & Labeling: Delivered high-accuracy verbatim and edited transcriptions for podcast episodes, business meetings, academic lectures, and voice assistant training data through Appen's transcription interfaces. Performed speaker diarization and timestamp alignment for multi-speaker audio. Labeled emotion recognition datasets for affective computing and customer sentiment analysis. Translation & Localization: Supported multilingual AI training through translation validation and localization quality assessment within Appen's workflow. Ensured cultural appropriateness and contextual accuracy for global deployment of voice assistants and content platforms across English, Kiswahili, and other languages. Geospatial Annotation: Performed precise geocoding and land cover

2024 - 2025

Education

Work History