Associate, ML Data Operations, GO-AI Operations

Amazon ADCI interviews typically start with an HR screening call, followed by an operational assessment where candidates annotate sample videos to demonstrate accuracy and speed. The final rounds focus on Amazon's Leadership Principles, with scenario‑based questions assessing problem‑solving, ownership, and customer obsession. Candidates should prepare concrete STAR stories and practice quick, precise video‑analysis exercises.

Amazon (ADCI - Maharashtra) promotes a fast‑paced, data‑driven culture where employees are encouraged to take ownership and innovate. The work‑from‑home model offers flexibility, while the company provides clear growth ladders and regular feedback. Employees benefit from a collaborative environment that balances high performance expectations with supportive mentorship.

Bachelor's degree in any stream; freshers welcome; no minimum percentage specified; maximum one active backlog allowed; must be able to commit to rotational shifts and night work; Indian citizenship or work‑authorization for India.

Round 1: Screening call (HR) → Round 2: Operational assessment (video annotation test) → Round 3: Technical/behavioral interview (Amazon Leadership Principles) → Round 4: HR final discussion

Amazon, a global leader in e‑commerce and cloud computing, has a massive logistics network that powers its rapid delivery promise. In India, Amazon operates dozens of fulfillment centers and invests heavily in robotics and AI to streamline inventory handling. The ADCI (Amazon Development Centre India) in Maharashtra drives many of these innovations, focusing on building intelligent systems that can see, learn, and act within the warehouse environment. Employees at ADCI work on cutting‑edge projects that directly impact millions of customers, enjoying a culture that prizes customer obsession, ownership, and continuous learning. The role of Associate, ML Data Operations – GO‑AI Operations is a virtual, work‑from‑home position that supports Amazon Robotics fulfillment centers across the country. As an associate, you will be the eyes and ears of the AI models that guide robotic stowing and picking. By watching short video clips of real‑world stowing actions and accurately annotating product locations, you help train and validate computer‑vision algorithms that keep inventory counts precise and reduce manual errors. This operational role does not require a technical degree, but it demands meticulous attention to detail and a steady focus on screen‑based tasks. Key responsibilities include: 1. Watching and auditing several hundred 15‑20 second videos per shift using internal annotation tools. 2. Verifying or marking product locations with high accuracy to improve AI model training. 3. Maintaining predefined quality (accuracy) and productivity (speed) targets. 4. Following a structured break schedule to achieve 6.8–7 hours of active work daily. 5. Working rotational shifts, including night shifts, in a 24x7 operational model. 6. Setting up a dedicated home workspace with proper lighting, table, and chair. 7. Attending virtual meetings with camera on when required. 8. Occasionally visiting the office for onboarding or team events. 9. Reporting any tool glitches or ambiguous video content to the operations lead. 10. Continuously seeking ways to improve annotation speed without compromising quality. The tech stack for this role is lightweight: Amazon’s proprietary data‑labeling platform, basic spreadsheet tools, and secure VPN access. While coding is not mandatory, familiarity with Excel or simple scripting can help automate repetitive tasks. Career growth can lead to senior data‑operations roles, quality‑assurance leads, or transition into more technical positions such as ML data analyst or annotation specialist, especially for those who upskill in data‑science fundamentals. Why join Amazon? The company offers a globally recognized brand, exposure to AI‑driven logistics, and a clear pathway for performance‑based promotions. Even as a fresher, you gain hands‑on experience with real‑world AI data pipelines, develop a disciplined work ethic, and become part of a supportive remote‑team culture that values diversity and inclusion. The night‑shift allowance, flexible work‑from‑home setup, and occasional office interactions provide a balanced yet dynamic work environment.