ML Data Associate- Hindi (Contractual role)

Amazon’s interview process typically starts with an online assessment focusing on logical reasoning and basic quantitative skills, followed by a technical interview that evaluates data labeling knowledge, Hindi proficiency, and problem‑solving ability. The final HR round assesses cultural fit, motivation, and compensation expectations. Candidates should prepare with the STAR method for behavioral questions and practice scenario‑based labeling tasks.

Amazon promotes a high‑performance culture built on its Leadership Principles, encouraging ownership, customer obsession, and continuous learning. Employees enjoy clear growth pathways, frequent feedback, and the chance to work on large‑scale, impactful projects, while maintaining a balanced approach to work‑life integration.

Bachelor's degree in any discipline (Engineering, Science, Arts, Commerce); minimum 60% aggregate; fluent in Hindi (B2/C1 level); no backlogs at the time of joining; must have all graduation certificates; 0‑3 years of relevant experience acceptable.

Round 1: Online Assessment (aptitude & logical reasoning) → Round 2: Technical Interview (data labeling concepts, Hindi proficiency, scenario‑based questions) → Round 3: HR Interview (fit, motivation, compensation discussion)

Amazon, a global leader in e‑commerce and cloud computing, has grown into one of the most innovative technology companies in the world. With a relentless focus on customer obsession, Amazon continuously invests in cutting‑edge technologies such as generative AI, machine learning, and robotics to solve complex problems and improve everyday life. The company’s culture of ownership, bias for action, and long‑term thinking has created an environment where fresh talent can quickly make an impact and grow alongside industry pioneers. The role of ML Data Associate – Hindi is a 12‑month contractual position based in Hyderabad, where you will be part of Amazon’s AI‑driven data labeling team. You will work on multimodal datasets—text, speech, audio, image, and video—ensuring that the data fed into Amazon’s large language models (LLMs) is accurate, unbiased, and of the highest quality. This position offers a unique opportunity to contribute directly to the development of responsible AI solutions that power services used by millions worldwide. **Key Responsibilities** 1. Maintain strict confidentiality of customer data and adhere to Amazon’s privacy policies. 2. Label and evaluate multimodal data (text, speech, audio, image, video) using in‑house tools and detailed guidelines. 3. Deliver high‑quality labeled data that meets defined KPIs and quality standards. 4. Generate insightful human‑generated data across various modalities to improve model performance. 5. Make sound judgments when faced with ambiguous or incomplete information. 6. Switch seamlessly between different labeling categories while maintaining attention to detail. 7. Support daily operational deliverables for multiple task types and assist teammates as needed. 8. Analyse root causes of errors, identify patterns, and propose process improvements. 9. Identify operational bottlenecks in SOPs and tools, suggesting enhancements to unblock workflows. 10. Provide floor support by clarifying internal queries during execution. **Tech Stack & Tools**: Amazon‑internal labeling platforms, basic scripting (Python/SQL) for data validation, familiarity with audio/video annotation tools, and proficiency in Hindi language processing. **Growth Path**: Successful associates can transition to full‑time roles within Amazon’s Machine Learning Operations (MLOps) teams, move into data science, product management, or specialized AI research positions. The exposure to real‑world AI pipelines accelerates career progression. **Why Join Amazon?** - Work on cutting‑edge AI projects that impact millions of customers. - Learn from seasoned engineers and researchers in a fast‑paced, data‑driven environment. - Competitive compensation, comprehensive benefits, and a culture that rewards ownership and innovation. - Opportunity to develop multilingual AI capabilities, a high‑growth area in the tech industry.