Dennis Gicugu

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Audio

Text

Top Label Types

Bounding Box

Point Key Point

Segmentation

Classification

Tracking

Emotion Recognition

Text Summarization

Evaluation Rating

Transcription

Text Generation

Red Teaming

Prompt Response Writing SFT

Freelancer Overview

I am an experienced AI Trainer and Computer Vision Data Specialist with over three years supporting AI/ML production teams through high-precision image, video, and audio annotation. My work has focused on preparing structured datasets for object detection and multi-object tracking systems, particularly using YOLOv5 and YOLOv8 frameworks. I am skilled in a variety of annotation platforms including Labelbox, CVAT, Supervisely, and VGG Image Annotator, and have maintained QA approval rates above 98% by optimizing workflows and ensuring dataset integrity. My expertise spans bounding box, polygon, and keypoint annotation, dataset formatting (COCO, Pascal VOC, JSON), and supporting audio/NLP tasks such as transcription and speaker diarization. I thrive in distributed, remote environments, collaborating closely with ML engineers to refine labeling edge cases and deliver high-quality training data for computer vision and speech recognition models.

Labeling Experience

Speech-to-Text (ASR) Transcription & Audio Annotation for AI Model Training

LabelboxAudioEmotion RecognitionText Summarization

Contributed to large-scale Automatic Speech Recognition (ASR) and NLP training projects involving high-accuracy transcription, speaker labeling, and audio segmentation. Key Responsibilities: Transcribed and timestamped 2,000+ hours of audio data across diverse accents and acoustic environments. Performed speaker diarization and labeled multi-speaker conversations with high precision. Cleaned and normalized transcripts to meet strict formatting and linguistic standards. Flagged unclear audio segments and applied structured noise-labeling protocols. Evaluated AI-generated transcripts and rated accuracy for model refinement. Assisted in emotion tagging and sentiment labeling for conversational AI datasets. Maintained 98%+ transcription accuracy based on QA audits. Quality Measures Followed: Adherence to verbatim and clean-verbatim transcription standards Timestamp consistency and alignment validation Multi-stage review and correction workflow Consistent terminology normalizat

Contributed to large-scale Automatic Speech Recognition (ASR) and NLP training projects involving high-accuracy transcription, speaker labeling, and audio segmentation. Key Responsibilities: Transcribed and timestamped 2,000+ hours of audio data across diverse accents and acoustic environments. Performed speaker diarization and labeled multi-speaker conversations with high precision. Cleaned and normalized transcripts to meet strict formatting and linguistic standards. Flagged unclear audio segments and applied structured noise-labeling protocols. Evaluated AI-generated transcripts and rated accuracy for model refinement. Assisted in emotion tagging and sentiment labeling for conversational AI datasets. Maintained 98%+ transcription accuracy based on QA audits. Quality Measures Followed: Adherence to verbatim and clean-verbatim transcription standards Timestamp consistency and alignment validation Multi-stage review and correction workflow Consistent terminology normalizat

2023 - 2025

LLM Evaluation, RLHF & Prompt-Response Data Annotation for AI Model Alignment

LabelboxTextText GenerationEvaluation Rating

Contributed to Large Language Model (LLM) training and alignment projects focused on improving response quality, safety, and instruction-following capabilities. Key Responsibilities: Evaluated AI-generated responses based on helpfulness, accuracy, coherence, and safety guidelines. Ranked multiple model outputs to support Reinforcement Learning from Human Feedback (RLHF) pipelines. Wrote high-quality prompt-response pairs for supervised fine-tuning (SFT) datasets. Identified hallucinations, logical inconsistencies, and factual inaccuracies in model outputs. Conducted red-teaming tasks to test model robustness and policy compliance. Applied structured rating rubrics to ensure consistent scoring across datasets. Provided detailed qualitative feedback to improve model alignment. Project Scope: Evaluated and rated 5,000+ model responses across diverse domains (general knowledge, reasoning, conversational tasks). Contributed to iterative model improvement cycles through structured

Contributed to Large Language Model (LLM) training and alignment projects focused on improving response quality, safety, and instruction-following capabilities. Key Responsibilities: Evaluated AI-generated responses based on helpfulness, accuracy, coherence, and safety guidelines. Ranked multiple model outputs to support Reinforcement Learning from Human Feedback (RLHF) pipelines. Wrote high-quality prompt-response pairs for supervised fine-tuning (SFT) datasets. Identified hallucinations, logical inconsistencies, and factual inaccuracies in model outputs. Conducted red-teaming tasks to test model robustness and policy compliance. Applied structured rating rubrics to ensure consistent scoring across datasets. Provided detailed qualitative feedback to improve model alignment. Project Scope: Evaluated and rated 5,000+ model responses across diverse domains (general knowledge, reasoning, conversational tasks). Contributed to iterative model improvement cycles through structured

2022 - 2025

YOLO-Based Object Detection & Multi-Object Tracking Dataset Preparation

LabelboxVideoBounding BoxPoint Key Point

Led large-scale image and video annotation projects supporting YOLOv5 and YOLOv8 object detection models. The project involved preparing structured datasets for real-world object detection and multi-object tracking applications. Key Responsibilities: Annotated 15,000+ images and 1,000+ video sequences using bounding boxes, polygons, and object tracking techniques. Labeled multi-class objects including occluded, fast-moving, and low-light instances. Implemented frame-by-frame object tracking across video sequences to maintain ID consistency. Structured datasets in COCO and Pascal VOC formats for model ingestion. Conducted tiered QA reviews and maintained a 98–99% quality approval rate. Improved annotation workflow efficiency by optimizing Labelbox review pipelines. Collaborated with ML engineers to refine labeling guidelines based on model error analysis. Quality Measures Followed: Strict adherence to annotation guidelines Inter-annotator agreement monitoring Multi-stage rev

Led large-scale image and video annotation projects supporting YOLOv5 and YOLOv8 object detection models. The project involved preparing structured datasets for real-world object detection and multi-object tracking applications. Key Responsibilities: Annotated 15,000+ images and 1,000+ video sequences using bounding boxes, polygons, and object tracking techniques. Labeled multi-class objects including occluded, fast-moving, and low-light instances. Implemented frame-by-frame object tracking across video sequences to maintain ID consistency. Structured datasets in COCO and Pascal VOC formats for model ingestion. Conducted tiered QA reviews and maintained a 98–99% quality approval rate. Improved annotation workflow efficiency by optimizing Labelbox review pipelines. Collaborated with ML engineers to refine labeling guidelines based on model error analysis. Quality Measures Followed: Strict adherence to annotation guidelines Inter-annotator agreement monitoring Multi-stage rev

2022 - 2025

YOLO-Based Object Detection & Multi-Object Tracking Dataset Preparation

LabelboxImageBounding BoxPoint Key Point

Led large-scale image and video annotation projects supporting YOLOv5 and YOLOv8 object detection models. The project involved preparing structured datasets for real-world object detection and multi-object tracking applications. Key Responsibilities: Annotated 15,000+ images and 1,000+ video sequences using bounding boxes, polygons, and object tracking techniques. Labeled multi-class objects including occluded, fast-moving, and low-light instances. Implemented frame-by-frame object tracking across video sequences to maintain ID consistency. Structured datasets in COCO and Pascal VOC formats for model ingestion. Conducted tiered QA reviews and maintained a 98–99% quality approval rate. Improved annotation workflow efficiency by optimizing Labelbox review pipelines. Collaborated with ML engineers to refine labeling guidelines based on model error analysis. Quality Measures Followed: Strict adherence to annotation guidelines Inter-annotator agreement monitoring Multi-stage rev

Led large-scale image and video annotation projects supporting YOLOv5 and YOLOv8 object detection models. The project involved preparing structured datasets for real-world object detection and multi-object tracking applications. Key Responsibilities: Annotated 15,000+ images and 1,000+ video sequences using bounding boxes, polygons, and object tracking techniques. Labeled multi-class objects including occluded, fast-moving, and low-light instances. Implemented frame-by-frame object tracking across video sequences to maintain ID consistency. Structured datasets in COCO and Pascal VOC formats for model ingestion. Conducted tiered QA reviews and maintained a 98–99% quality approval rate. Improved annotation workflow efficiency by optimizing Labelbox review pipelines. Collaborated with ML engineers to refine labeling guidelines based on model error analysis. Quality Measures Followed: Strict adherence to annotation guidelines Inter-annotator agreement monitoring Multi-stage rev

2022 - 2025

Education

Work History