Inda Rince

Key Skills

Software

Appen

CVAT

Data Annotation Tech

Labelbox

Mindrift

Remotasks

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Task Types

Bounding Box

Classification

Data Collection

Evaluation Rating

Prompt Response Writing SFT

Freelancer Overview

I am an experienced AI Data Labeling and Annotation Specialist with a strong background in supporting the training of large-scale AI systems across text, image, and audio datasets. My expertise includes classification, bounding box annotation, segmentation, entity recognition (NER), and evaluation/rating tasks. I have contributed to projects involving LLM evaluation, prompt and response generation, summarization, and red-teaming to improve the quality and safety of generative AI models. In addition, I am proficient with industry-leading labeling tools such as Label Studio, Labelbox, CVAT, Roboflow, and SuperAnnotate, and have collaborated on projects in computer vision, natural language processing, and speech recognition. With fluency in English and Swahili, I bring both technical accuracy and multilingual versatility, making me well-suited for AI training projects across diverse industries and subject matter domains.

Labeling Experience

prompt evaluation and writing

Scale AITextText SummarizationData Collection

Worked on programming-focused data labeling projects through Remotasks, where the scope involved annotating and validating datasets for computer science and AI model training. Specific tasks included code classification, function labeling, debugging assistance annotations, and prompt–response writing for programming tasks. The projects required attention to detail in identifying correct logic, syntax, and expected outputs across multiple coding languages. Collaborated in large-scale datasets aimed at improving AI’s ability to understand and generate programming solutions. Maintained high quality standards by following strict task guidelines, performing peer reviews, and ensuring accuracy and consistency above 95% benchmark quality metrics. Successfully contributed to projects in clinical medicine and computer science applications, supporting the development of reliable AI systems.

Worked on programming-focused data labeling projects through Remotasks, where the scope involved annotating and validating datasets for computer science and AI model training. Specific tasks included code classification, function labeling, debugging assistance annotations, and prompt–response writing for programming tasks. The projects required attention to detail in identifying correct logic, syntax, and expected outputs across multiple coding languages. Collaborated in large-scale datasets aimed at improving AI’s ability to understand and generate programming solutions. Maintained high quality standards by following strict task guidelines, performing peer reviews, and ensuring accuracy and consistency above 95% benchmark quality metrics. Successfully contributed to projects in clinical medicine and computer science applications, supporting the development of reliable AI systems.

2024 - 2024

Education

Work History