Telugu Team Lead with Mathematics

Role overview

Quality monitoring and communication

• Spot-check Telugu items, identify quality issues, provide feedback, and escalate recurring or critical problems.
• Update trainers and QAs about guidelines, project changes, workflow updates, and quality expectations through Discord.
• Answer questions about Telugu wording, register, translation fidelity, cultural context, regional usage, and edge cases.
• Contact inactive contributors, encourage activation, track follow-ups, and flag availability issues.

Documentation, onboarding, and process

• Create and maintain style guides, trackers, FAQs, quality notes, examples, honeypots, and onboarding materials.
• Run onboarding and training calls covering expectations, workflows, rubrics, quality standards, and Telugu-specific requirements.
• Ensure consistent application of Telugu guidelines and communicate updates as projects evolve.
• Identify recurring quality gaps, propose improvements, and help build scalable QA processes.

Required qualifications

• Bachelor’s or Master’s degree in Mathematics.
• Native or near-native Telugu proficiency with strong reading and writing skills, and strong English for team communication and feedback.
• At least 3 years of experience in Telugu writing, editing, translation, localization, content QA, AI training, education, annotation, or related language-review work.
• Strong knowledge of Telugu grammar, spelling, punctuation, tone, register, regional variation, and cultural context.
• Able to evaluate content against detailed rubrics and identify mistranslation, unnatural language, hallucinated claims, ambiguity, and inconsistent terminology.
• Comfortable with remote work and tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
• Highly detail-oriented and organized, with the ability to maintain quality documentation.

Preferred experience

• Experience leading or supporting remote teams of trainers, annotators, reviewers, editors, or QAs is strongly preferred.
• Experience with AI training, data annotation, large language models, prompt/response evaluation, or rubric-based LLM QA is a strong plus.

Selection process

• The selection process includes an AI interview, a domain-specific task, and a recruiter interview.

About the company