Dobgima F.

Key Skills

Software

Labelbox

CVAT

Scale AI

Prodigy

Top Subject Matter

Engineering and technical datasets (computer vision for AI training)

NLP dataset preparation for LLM training (classification, sentiment, labeling ontology)

Top Data Types

Image

Text

Video

Top Task Types

Bounding Box

Fine-tuning

Classification

Freelancer Overview

Data Annotation & Labelling Specialist (Freelance/Remote). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox, CVAT, and Scale AI. Education includes Higher National Diploma, HHIST Bamenda (2025) and Continuing Education Program, N/A (2025). AI-training focus includes data types such as Image and Text and labeling workflows including Bounding Box and Fine-tuning.

Labeling Experience

Data Annotation & Labelling Specialist (Freelance/Remote)

LabelboxTextFine-tuning

Prepared NLP datasets for text classification, sentiment tagging, and LLM fine-tuning pipelines using structured labeling frameworks. Ensured labels conformed to defined annotation guidelines and targeted consistent data representations for training. Coordinated with technical stakeholders by flagging edge cases and ensuring the stability of labeling ontology for large-scale annotation batches. • Built and validated text/NLP labels for classification and sentiment tasks. • Applied structured labeling frameworks to support LLM fine-tuning workflows. • Ensured annotation guideline compliance and cross-annotator alignment. • Escalated ambiguous samples to maintain ontology integrity.

2025 - Present

Data Annotation & Labelling Specialist (Freelance/Remote)

LabelboxImageBounding Box

Annotated engineering and technical imagery using bounding boxes, polygons, and related markup to support training data readiness for computer vision models. Followed strict project-specific annotation guidelines to achieve consistent, high-quality labels suitable for downstream ML training. Documented and escalated edge cases and ambiguous samples to ensure ontology integrity and maintain accuracy across large batch tasks. • Performed image bounding box labeling, polygon marking, and segmentation-style annotations. • Maintained cross-annotator consistency through adherence to labeling frameworks and QA checks. • Used spreadsheet-based QA logs to track throughput, error rates, and review cycles. • Reported ambiguous samples and edge cases for data science lead review.

2025 - Present

Education

Work History