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Mumbai, Maharashtra, India
Onsite
Would you like to be part of a team that delivers high-quality software to our customers?
Are you a visible champion with a ‘can do’ attitude and enthusiasm that inspires others?
About the Business
LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti-Money Laundering/Counter Terrorist Financing, Identity Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management. You can learn more about LexisNexis Risk at the link below, https://risk.lexisnexis.com
About the Team
You’ll join a collaborative engineering team building scalable, high-quality software solutions across backend, frontend, and cloud. The team works closely with cross-functional partners, values knowledge sharing and continuous learning, and encourages everyone to contribute ideas and improvements while balancing technical excellence with delivery.
About the Role
We are seeking detail-oriented AI Annotators to support the development of secure and fair identity verification systems. Your work will involve labeling and validating datasets used in document fraud detection, deepfake detection, presentation attack detection (PAD), and face recognition. The annotations you provide will directly improve model accuracy, fairness, and resilience.
Responsibilities:
Data annotation across multiple formats (documents, images, video, audio) following project guidelines.
Identify and label document elements (e.g., names, photos, signatures, holograms) and flag potential tampering.
Annotate manipulated regions in deepfake and image tamper datasets.
Tag liveness indicators and spoof artifacts in PAD datasets.
Draw bounding boxes, facial landmarks, and expression labels for face recognition tasks.
Apply time-based labels in video for frame-level PAD/deepfake detection.
Document edge cases and suggest improvements to annotation guidelines.
Review peer annotations, check consistency, and ensure quality standards.
Collaborate with ML engineers and data scientists to improve dataset quality and tools.
Requirements:
Attention to detail and strong observational skills.
Ability to focus on repetitive tasks while maintaining accuracy.
Basic understanding of computer vision/ML concepts (training provided).
Comfortable using web-based annotation tools (training provided).
Clear written and verbal communication skills to describe edge cases.
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