Hindi/English Bilingual AI Data Annotator &

About the Role

This is a fully remote, hourly contractor role supporting AI data and language projects on a project-based, flexible hour basis.

Key Responsibilities

• Data annotation: labeling, classifying, and structuring documents, tables, and other content to support AI training datasets.
• LLM evaluation: reviewing AI-generated responses for accuracy, reasoning quality, coherence, and cultural/linguistic appropriateness.
• Content generation: writing high-quality prompts and model responses, or recording high-quality voice samples, to guide AI learning across diverse topics.
• Localization QA: ensuring terminology, tone, cultural nuance, and locale-specific details (units, references, names, dates) are consistently accurate across outputs.

Requirements & Tech Stack

• Native or near-native Hindi proficiency with strong writing and editing skills.
• Minimum C1 English proficiency (reading/writing) for interpreting prompts, source materials, and evaluation guidelines.
• Excellent editorial judgment in register, tone, punctuation, inclusivity, and cultural nuance, with extreme attention to detail.
• Ability to identify meaning drift, ambiguity, locale inconsistencies, and subtle errors, and to explain corrections clearly in writing.
• Ability to rigorously fact-check localized content (units, references, names, dates) using reliable sources and consistent reasoning.
• Ability to identify reasoning gaps, methodological errors, and unclear explanations even when language is fluent.
• Reliable, self-directed, and able to deliver consistent quality with clear communication and responsiveness across time zones.
• Apply senior-level Hindi localization and translation judgment alongside minimum C1 English proficiency.

Qualifications

• Bachelor's degree or higher, preferably in Translation, Linguistics, Localization, Communications, or a related field.

Preferred Experience

• Professional localization or translation experience.
• Previous experience with AI data training, annotation, or evaluation.
• Familiarity with MQM/LQA concepts (severity, category, and root-cause thinking) for consistent quality decisions.
• Familiarity with CAT tools and QA workflows.