Quality Assurance Lead (Gujarati)

As a Gujarati Quality Assurance Lead, you review AI-generated Gujarati content and trainer submissions against project guidelines and detailed rubrics. You provide clear written feedback, track recurring issues, and help keep quality consistent across remote contributors.

• Review Gujarati content for accuracy, fluency, grammar, spelling, tone, cultural appropriateness, meaning preservation, formatting, and instruction-following.
• Evaluate trainer and quality assurance work, identify repeated problems, and give feedback that helps contributors improve.
• Support onboarding and maintain style guides, FAQs, trackers, dashboards, and other quality materials.
• Help coordinate quality workflows and improve processes for Gujarati-language AI training projects.

What it pays and takes

This is a remote, part-time contract role for someone with strong Gujarati language-review skills and experience working with structured quality standards.

• Pay: $30 per hour.
• Time: 20+ hours per week.
• Location: Open to candidates in India, the United Kingdom, and the United States.
• Language: Fluent Gujarati with strong reading and writing ability, plus strong English communication skills.
• Experience: Background in Gujarati writing, editing, translation, localization, content quality assurance, AI training, annotation, or related language-review work.
• Tools: Experience with Discord, Google Sheets, Google Docs, trackers, dashboards, or similar workflow tools.
• Work style: Detail-oriented, organized, and comfortable following rubrics.
• Helpful background: Experience supporting remote trainers, annotators, reviewers, editors, or quality assurance contributors; familiarity with AI training, data annotation, large language models, prompt evaluation, or response evaluation.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training work is the human side of building artificial intelligence. People review examples, rate model responses, and improve language data so AI systems can produce more accurate and useful results; experienced reviewers are paid for applying careful subject and language judgment.