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REQUIREMENT_ID_1382 • 3-DAY_ACTIVE_POLICY

Associate, ML Data Operations, GO-AI Operations

Company
Company Amazon
Type
Opportunity Type Internship
Salary
Stipend / Salary ₹12 LPA (approx.)
Location
Location Maharashtra, India (Remote possible)
Posted Date
Posted Date Sep 28, 2026
Attention to detail video analysis data annotation basic computer proficiency ability to work in shifts strong communication teamwork problem‑solving time management
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Amazon Placement Papers Open Resource ↗
Collection of previous placement papers that help understand the type of questions asked for Amazon roles.
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Amazon Recruitment Process Experiences Open Resource ↗
First‑hand experiences from candidates detailing each interview round and preparation tips.
📖
Amazon Interview Preparation Guide Open Resource ↗
Comprehensive guide covering Amazon's interview format, common questions, and effective strategies.
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Algorithm Practice Problems Open Resource ↗
Extensive problem set to sharpen coding and logical reasoning skills useful for Amazon assessments.

Bachelor's degree in any discipline with minimum 60% aggregate (or CGPA 6.0/10). No active backlogs at the time of joining. Fresh graduates from 2024‑2026 batches are preferred. Must be legally eligible to work in India and able to commit to a 6‑month contract.

1
Round 1: Online assessment or screening questionnaire
2
Round 2: Technical/role‑play interview focusing on data annotation and problem‑solving
3
Round 3: Behavioral interview based on Amazon Leadership Principles
4
HR round for compensation and joining formalities
Amazon is a global e‑commerce and cloud‑computing giant that has built a reputation for relentless customer obsession, innovation, and operational excellence. In India, Amazon operates massive fulfillment centers, AI‑driven logistics networks, and a suite of digital services that touch millions of users daily. The company’s culture emphasizes ownership, data‑driven decision making, and a bias for action, making it a fertile ground for fresh talent to grow quickly. Amazon’s commitment to diversity and inclusion ensures that every employee, regardless of background, can contribute to its mission of being the Earth’s most customer‑centric company. The role of Associate, ML Data Operations – GO‑AI Operations is part of the Data Auditing Operations team that supports Amazon Robotics fulfillment centers. As an associate, you will watch short video clips of stowing actions, interpret the visual data, and accurately mark product locations using internal tools. Your work directly influences the quality of automated stow processes, helping to reduce errors and improve inventory accuracy across Amazon’s fulfillment network. This is a contract position of six months, with the possibility of remote work, but occasional on‑site presence may be required. Key Responsibilities: 1. Review hundreds of 15‑20 second video clips per shift and annotate product placement accurately. 2. Maintain high quality scores by adhering to predefined accuracy and productivity targets. 3. Identify details in low‑resolution or blurry videos using keen observation skills. 4. Follow rotational shift schedules, including night shifts, and take mandatory breaks. 5. Ensure a dedicated, distraction‑free workspace when working from home. 6. Switch on laptop camera during virtual meetings and collaborate with remote teammates. 7. Meet incremental performance goals for quality and speed. 8. Provide feedback to the automation team to improve AI models. 9. Comply with Amazon’s data security and confidentiality policies. 10. Attend periodic training sessions and performance coaching. Tech Stack & Tools: The role primarily uses Amazon‑internal annotation platforms, video playback tools, and basic office productivity software. No deep programming knowledge is required, but familiarity with data labeling concepts and a comfort with Windows/macOS environments is beneficial. Growth Path: High‑performing associates can transition to full‑time roles in data science, operations management, or quality assurance within Amazon’s supply‑chain ecosystem. The exposure to AI‑driven processes provides a solid foundation for future roles in machine‑learning operations or robotics. Why Join Amazon? You will be part of a world‑class logistics operation, gain hands‑on experience with cutting‑edge AI auditing workflows, and develop a disciplined work ethic under Amazon’s leadership principles. The role offers competitive compensation, night‑shift allowances, and the chance to contribute directly to the efficiency of one of the largest fulfillment networks in the world.

Amazon — AI & Machine Learning Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC 📖
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

Open Resource ↗
COMPANY GUIDE 🎯
Amazon Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Amazon and off-campus tech roles.

Open Resource ↗
MOCK TESTS & PAPERS 📝
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource ↗
CODING PRACTICE 💻
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource ↗
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (Attention to detail, video analysis, data annotation, basic computer proficiency, ability to work in shifts, strong communication, teamwork, problem‑solving, time management) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
Amazon Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the bias-variance tradeoff and how do you prevent overfitting? Answer ▼
Model Answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer ▼
Model Answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer ▼
Model Answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer ▼
Model Answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Why do you want to join Amazon as a Associate, ML Data Operations, GO-AI Operations?
Preparation Tip: Highlight Amazon's market reputation, recent tech innovations, and how your skills in Attention to detail directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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