Putri Rizkiyah
I’m Putri Rizkiyah, a Biology Education graduate from Universitas Negeri Surabaya (UNESA) with experience supporting AI/ML training through accurate, guideline-compliant data annotation.
About
I’m Putri Rizkiyah, a Biology Education graduate from Universitas Negeri Surabaya (UNESA) with experience supporting AI/ML training through accurate, guideline-compliant data annotation. I work across image (bounding boxes, segmentation, and structured tagging), video (temporal action localization, micro-activity labeling, and quality scoring), audio (speech attributes like pace, naturalness, intonation, intent, gender, and age group), and text (emotion, intent, and sentiment labeling).
Beyond annotation, I also contribute as an AI auditor and QA specialist—reviewing dataset quality, correcting labeling errors, and evaluating rater objectivity to reduce bias and scenario failures. I enjoy detail-oriented work that improves dataset reliability and helps ensure AI outputs meet strict standards, including hallucination detection and output quality assessment.
- 4 years' experience
- Joined October 2025
Skills· 15
Languages
- EnglishFluent
- JavaneseFluent
Industry experience
- Education
- Media & Entertainment
- Software & Internet
- Professional Services
- Other
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Work experience· 14 roles
Conducted independent audits and QA reviews for annotated datasets and AI speech evaluations to ensure accuracy, consistency, and full compliance with project guidelines. Evaluated objectivity of rater assessments in AI voice interactions using rigorous audit taxonomies to identify bias, scenario failures, and factual inaccuracies. Corrected labeling errors and provided structured feedback to raters/annotators. Audited AI model responses for output quality, including hallucination detection and trajectory analysis aligned to project guidelines.
Conduct independent QA audits on annotated datasets and AI speech evaluations to ensure accuracy, consistency, and full compliance with project guidelines. Evaluate rater objectivity in human–AI voice interactions using audit taxonomies to identify bias, scenario failures, and factual inaccuracies. Correct labeling errors and provide structured feedback to raters and annotators to improve dataset reliability. Audit AI model responses for hallucination and conduct trajectory analysis to verify strict adherence to project guidelines.
Performed quality assurance checks and reviewed datasets for labeling errors.
Education
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