Gujarati Language Expert
SME Careers
Remote
This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your Gujarati expertise directly helps improve the world’s premier AI models by making their Gujarati language output more natural, reliable, culturally accurate, and clearly written.
Key Responsibilities
- Develop AI Training Content: Create prompts and gold-standard Gujarati answers across multiple domains (general knowledge, customer support, education, conversational writing).
- Optimize AI Performance: Evaluate and rank AI outputs to improve fluency, correctness, tone match, and cultural appropriateness.
- Ensure Model Integrity: Detect unsafe or misleading content, factual hallucinations, bias, and policy-risk language; validate reliability across use cases.
- Bachelor’s degree (or higher) in Linguistics, Gujarati/English Literature, Communications, Journalism, Translation, or a related field (or equivalent professional experience).
- Native or near-native Gujarati proficiency (reading and writing required).
- Strong professional proficiency in English (minimum C1) to follow guidelines and provide detailed feedback in English.
- 3+ years of professional experience in writing, editing, localization, translation, content QA, journalism, education, or related language-focused work.
- Deep understanding of Gujarati grammar, spelling conventions, punctuation, and style—able to explain why something is wrong and how to fix it.
- Comfortable handling multiple registers (formal/informal), dialect variation, and audience adaptation (e.g., India vs diaspora audiences).
- High attention to detail when spotting subtle meaning shifts, hallucinated claims, or culturally awkward phrasing.
- Reliable, self-directed, and able to deliver consistent quality in an hourly, remote contractor workflow across time zones.
- Prior experience with AI data training/annotation, translation QA, editorial QA, or rubric-based evaluation is strongly preferred.