Associate, ML Data Operations (GO-AI)

Amazon’s interview process typically starts with an online aptitude test, followed by 2–3 technical rounds focusing on data handling, Python/SQL, and basic ML concepts. The final HR round assesses cultural fit and communication. Candidates should practice problem‑solving, be ready to discuss data‑quality scenarios, and demonstrate a customer‑obsessed mindset.

Amazon promotes a fast‑paced, data‑driven work culture where ownership and customer obsession are core values. Employees enjoy abundant learning resources, clear growth pathways, and a collaborative environment that balances challenging projects with work‑life flexibility, especially in remote roles.

Any Bachelor's Degree (any discipline), Minimum 60% aggregate (or CGPA 6.0/10), No active backlogs at the time of joining, Open to all batch years (freshers welcome), 0–1 year of experience (freshers eligible).

Round 1: Online assessment (aptitude and logical reasoning) → Round 2: Technical interview (data handling, Python/SQL, ML basics) → Round 3: Managerial interview (role fit, communication) → Round 4: HR interview (culture fit, compensation discussion)

Amazon, founded in 1994, has grown from an online bookstore to a global technology and e‑commerce leader with a presence in over 20 countries. In India, Amazon operates massive fulfillment centers, cloud services through AWS, and a suite of consumer‑facing products that touch millions of daily users. The company’s culture of customer obsession, innovation, and long‑term thinking makes it a top destination for fresh talent seeking to work on cutting‑edge solutions at scale. The role of Associate, ML Data Operations (GO‑AI) is part of Amazon’s growing focus on machine‑learning‑driven products and services. As a fresher‑friendly position, it offers a unique entry point into the world of AI, where you will support data pipelines that power recommendation engines, computer‑vision models, and natural‑language systems. The job is 100 % remote, allowing you to work from any of the listed Indian cities while collaborating with cross‑functional teams across the globe. Key responsibilities include: 1. Curate, clean, and label large datasets used for training ML models. 2. Perform quality checks and validation of annotated data to ensure high‑accuracy standards. 3. Collaborate with data scientists and engineers to understand labeling requirements and provide feedback. 4. Use internal tools and AWS services (S3, SageMaker Ground Truth) for data storage and annotation workflows. 5. Document data‑operation processes and maintain version‑controlled metadata. 6. Identify and resolve data inconsistencies, missing values, and bias issues. 7. Assist in building and maintaining dashboards for data‑quality metrics. 8. Participate in regular sprint meetings, reporting progress and blockers. 9. Contribute ideas for automation of repetitive labeling tasks. 10. Support the GO‑AI team in preparing data for model evaluation and A/B testing. Tech stack commonly involves Python, SQL, Excel, AWS S3, SageMaker Ground Truth, and basic scripting for automation. Over time, high‑performing associates can move into senior data‑operations roles, data‑engineering positions, or transition to ML‑engineer tracks, benefitting from Amazon’s robust internal learning platforms and mentorship programs. Why join Amazon? You gain exposure to industry‑leading AI projects, a supportive remote work environment, competitive compensation with night‑shift allowance, and a clear career ladder that rewards performance. The six‑month contract can serve as a springboard to a permanent role within Amazon’s vast ecosystem, giving freshers a fast‑track into the tech industry.