Brian Mundia

Annotate high-volume image, video, and text data using CVAT, Label Studio, and proprietary AI software. Lead a team of junior annotators, ensuring high annotation quality and conducting quality assurance checks to improve ML model accuracy.

Labeled medical and autonomous vehicle datasets, ensuring precise object detection and segmentation. Coordinated with data science teams to refine annotation guidelines and achieved a 98%+ accuracy rate for AI training datasets.

Tagged and classified social media sentiment data for NLP-based AI models. Conducted bounding box and instance segmentation annotations for image classification projects, improving voice recognition AI performance.

Annotated satellite images for geospatial AI projects and performed sentiment tagging for chatbot training data. Assisted in automated error detection, refining annotation workflows and improving efficiency.

Handled text annotation and document classification for AI-driven search engines. Participated in team training sessions and maintained error-free labeling logs to improve dataset consistency.

Achieved certification in Data Annotation with a focus on AI & ML training data, enhancing skills in data labeling and quality assurance.

Completed an advanced course in Computer Vision & NLP Annotation, gaining expertise in annotation techniques for AI models.

Completed a Bachelor's Degree in Data Science & AI, focusing on data analysis, machine learning, and artificial intelligence applications.