Контактная информация

Соискатель указал телефон и эл. почту.

Получить контакты этого кандидата можно на странице https://www.work.ua/resumes/17769078/

Опыт работы

Data annotator

с 01.2024 по 01.2026 (2 года)
Hugo Technologies, Київ (IT)

I have over three years of experience in AI and machine learning, specializing in data annotation and evaluation across text, image, audio, and video. I’ve audited more than 10,000 multimodal datasets, carefully checking for inconsistencies and quality issues to ensure reliable training data. My work has consistently supported model accuracy rates above 94%. Through this experience, I’ve developed strong analytical thinking, attention to detail, and quality assurance skills. I’m also experienced with RLHF evaluation and prompt engineering for LLMs.
My technical proficiency includes platforms such as Appen, Lionbridge, Scale Al, Labelbox, Prodigy, CVAT, and SuperAnnotate, making me adaptable and efficient in diverse Al projects.

Пауза в карьере

с 01.2019 по 12.2023 (5 лет)
Лечение и восстановление

Took time off for health related reasons. i have fully recovered, and available to resume professional duties, and ready to contribute effectively.

Образование

University of Lagos

Bachelor of Arts (BA), English Language, Odesa
Высшее, с 2012 по 2017 (5 лет)

Дополнительное образование и сертификаты

Нет дополнительного образования и сертификатов.

Знания и навыки

• Critical thinking
• Data analysis
• Productivity
• Accuracy
• Application of artificial intelligence
• Effective communication
• Attentiveness
• Customer
• Attracting new customers
• Initiative
• Receiving and processing orders
• Conducting online chats
• Production management

Знание языков

Английский — продвинутый

Дополнительная информация

Reviewing annotated data to ensure it meets quality standards and guidelines.
Annotate data to ensure artificial Al models are trained with accurate data.
Providing feedback and guidance to data annotators to improve their accuracy and efficiency.
Identifying and addressing issues or discrepancies in annotated data.
Analyzing data annotation results to identify trends, patterns, and areas for improvement.
Documenting quality assurance procedures and findings.
Participating in training sessions for new annotators.