AvasarAI

Data Coordinator - (Annotation, Review & MEL)

Wadhwani AI LEHS · 116 days ago

Verified 45 days agoData and AnalyticscontractfresherIndia-eligible
Not disclosed
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  • Annotate user queries sequentially within each session while considering previous queries and responses to maintain conversational continuity and context.

  • Translate corrected user queries into clear and grammatically accurate English while preserving the original meaning and intent.

  • Rephrase user queries into complete standalone English queries using the current query, chat history, and available metadata for effective answer generation.

  • Identify and map crops mentioned in queries to the standardized crop list and assign the appropriate advisory category based on query intent and context.

  • Ensure consistency, accuracy, completeness, and adherence to project annotation guidelines, SOPs, confidentiality, and quality standards.

  • Review annotated sessions to validate translations, rephrased queries, crop mapping, advisory category mapping, and contextual understanding incorporated during annotation.

  • Identify inconsistencies, ambiguities, missing information, annotation errors, and potential biases, and provide corrective feedback to improve annotation quality and dataset reliability.

  • Ensure standardized interpretation and uniformity across the dataset, with each session handled by a single annotator and independently reviewed for quality assurance.

  • Validate agricultural advisories, pest management protocols, and crop-related recommendations against established agronomic literature and extension advisories.

  • Create and maintain high-quality ground-truth datasets, document evaluation decisions, and follow standardized evaluation protocols.

  • Evaluate AI-generated responses for accuracy, relevance, completeness, and alignment with recommended agricultural practices.

  • Identify incorrect, incomplete, or misleading AI responses, flag knowledge gaps, and provide structured feedback to support model refinement and improvement.

  • Participate in calibration and review discussions to maintain evaluation consistency and support continuous enhancement of the AI system.

  • Perform additional tasks related to annotation, review, evaluation, and quality assurance as assigned by the supervisor or project team.

Requirements

  • Education: Master’s degree (Second or Third or Fourth year) or PhD (Any year) in Entomology, Plant Pathology or Agronomy from an accredited university or college. Students pursuing a degree can also participate in.

  • Experience: Experience working with or training farmers is required.

  • Domain Knowledge: Strong understanding of crops, common plant diseases, pest identification, and agronomic terminology. Ability to assess the correctness of crop-related annotations is essential.

  • Tools Availability: Access to a functional laptop and a reliable internet connection is mandatory.

  • Technical Skills: Proficient with multitasking in a split-screen setup, using Excel or Google Sheets, and performing data entry and copy-paste operations efficiently.

  • Communication Skills: Strong verbal and written communication skills to participate in team calls, deliver feedback effectively, and coordinate with the project team.

  • Collaboration: Ability to work effectively with geographically dispersed teams and in multicultural environments.

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