Description:

Artificial Intelligence (AI) is transforming industries, and Amazon is at the forefront of generative AI innovation. The ML Data Associate-II role is central to ensuring the quality and reliability of AI systems by creating and labeling datasets across multiple modalities—text, speech, audio, image, and video.

As an ML Data Associate, you will play a crucial role in dialogue evaluation and data labeling, ensuring accuracy, consistency, and confidentiality. Your work directly supports the responsible development and deployment of large language models (LLMs) and other AI systems that enhance customer experiences across Amazon.

Key Responsibilities:

• Work with different types of data (text, speech, audio, image, video) to generate high-quality labeled datasets.
• Maintain strict confidentiality in handling sensitive data.
• Ensure accuracy and attention to detail while labeling and evaluating data for AI training.
• Analyze labeling errors, identify root causes, and propose process improvements.
• Support operational deliverables across multiple task types in a fast-paced environment.
• Collaborate with peers by clarifying internal queries and ensuring process consistency.
• Suggest changes in SOPs, workflows, and tools to unblock and improve operations.

Key Technical Skills:

Data labeling, Speech/text/audio/video evaluation, Research and synthesis, Analytical problem solving, Microsoft Office Suite, Business writing, Process documentation

Requirements:

• Associate’s degree or equivalent work experience.
• Fluency in Hindi language.
• Strong business writing and professional correspondence skills.
• Advanced reading comprehension and ability to interpret complex instructions.
• Strong research, analytical thinking, and problem-solving skills.
• Attention to detail with ability to manage complex documents and tasks.

Preferred:

• Bachelor’s degree in a relevant field.
• C1 level proficiency in English.
• 2+ years of professional experience with proven execution capabilities.
• Experience using open-source resources for research.
• Project coordination or management experience (1+ years).
• Ability to manage cross-department stakeholder relationships.
• Proficiency with Microsoft Office Suite and common business tools.

A Day in the Life:

You’ll spend your day performing foundational labeling tasks such as speech-to-text analysis, dialogue evaluation, and content categorization across different data types. The role demands focus, multitasking, and adaptability while working in a highly collaborative and diverse team environment.