Data Annotator

Position: Data Annotator

Employment Type: Full Time

Experience: 02 Years in Data Annotation, Data Labeling, Content Moderation, or AI/ML Support roles (Freshers with relevant exposure are welcome)

Company Description

Scry AI is a research-led enterprise AI company that builds intelligent platforms for BFSI, insurance, logistics, and industrial enterprises to drive efficiency, insight, and compliance. Our product suite includes Auriga for conversational AI, Collatio for document intelligence, and Concentio for cognitive IoT and operational intelligence. These platforms help clients convert complex, fragmented data into actionable enterprise intelligence.

Role Overview

We are looking for a detail-oriented Data Annotator to support the development and continuous improvement of our AI models. This role is critical in ensuring the accuracy, consistency, and quality of labeled datasets used across document intelligence, NLP, computer vision, and enterprise AI workflows.

As a Data Annotator, you will focus on labeling, validating, and reviewing structured and unstructured data such as documents, text, images, and forms. You will follow defined annotation guidelines while maintaining high accuracy and consistency across large datasets.

This role is ideal for someone who is detail-driven, process-oriented, and interested in building real-world AI systems from the ground up.

Key Responsibilities

Data Annotation and Labeling

• Annotate and label datasets, including text, documents, images, and forms based on defined guidelines
• Perform entity tagging, classification, bounding boxes, and relationship mapping as required
• Ensure accuracy, consistency, and completeness in all annotation tasks

Quality Control and Review

• Review and validate annotated data to meet quality and compliance standards
• Identify inconsistencies, edge cases, and ambiguous samples
• Flag guideline gaps and provide feedback to improve annotation processes

Documentation and Process Adherence

• Follow annotation guidelines, SOPs, and quality benchmarks strictly
• Document edge cases and exceptions for continuous guideline improvement
• Maintain clear records of work completed and quality metrics

Collaboration and Continuous Improvement

• Work closely with team leads, QA reviewers, and data science teams
• Participate in calibration sessions and feedback loops
• Support dataset refinement for model training, evaluation, and retraining

Required Qualifications and Skills

• 02 years of experience in data annotation, labeling, or similar roles
• Strong attention to detail and ability to follow detailed instructions
• Comfortable working with large volumes of repetitive data tasks
• Basic understanding of AI/ML concepts is a plus
• Good written communication and documentation skills

Our Ideal Candidate

• Is patient, detail-oriented, and quality-focused
• Enjoys structured, process-driven work
• Is comfortable working independently with minimal supervision
• Is curious about how AI models are trained and improved
• Values accuracy, consistency, and continuous learning

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