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This team enables automation at Amazon Robotics Fulfillment centers. This team serves Amazon Internal Fulfillment Technologies & Robotics teams by enabling automation, which includes real-time & offline (image/video) data auditing services. One of the key contributions of this team is supporting the fulfillment centers in maintaining inventory accuracy.
An Associate in this role is required to watch the video of the stowing action at a fulfillment center, understand it thoroughly and make best use of human judgement in combination with the tools and resources to indicate the activity captured in the video. They are expected to verify or mark the location of product through a tool while maintaining highest level of accuracy. This process helps in maintaining the fulfillment center's stow quality. This is an operational role. Under general supervision, the Associate performs precise and thorough video/image audits with high degree of accuracy and speed, thus aiding defect reduction.
Key job responsibilities
The Associate has to watch several hundred videos in a shift and provide responses by following goals on accuracy (quality), speed (productivity) and right / acceptable practices. Associates are required to take breaks at the pre-defined slots and ensure 6.8 to 7 hours time per day is spent to answer the videos. Associates who are hired to work from home should maintain (1) dedicated workspace i.e., table, chair & sufficient lighting (2) workspace / work related data shouldn't be accessed by anyone other than employee
The Candidate Is Expected To Demonstrate
A day in the life
Associates work in 24x7 environment with rotational shifts. Associates would be working in a 9 hour shift, including pre-scheduled breaks. The shift timings would be subject to change every 3-4 months or as per business requirement. In case associate is working in night shift, night shift allowance will be provided as per applicable Amazon's work policy.
Weekly Offs: Rotational two-consecutive day off (it is a 5-day working week with 2 consecutive days off, not necessarily Saturday and Sunday) or as per business discretion.
Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking. We are driven by the excitement of building technologies, inventing products, and providing services that change lives. We embrace new ways of doing things, make decisions quickly, and are not afraid to fail. We have the scope and capabilities of a large company, and the spirit and heart of a small one.
Together, Amazonians research and develop new technologies from Amazon Web Services to Alexa on behalf of our customers: shoppers, sellers, content creators, and developers around the world.
Our mission is to be Earth's most customer-centric company. Our actions, goals, projects, programs, and inventions begin and end with the customer top of mind.
You'll also hear us say that at Amazon, it's always "Day 1." What do we mean That our approach remains the same as it was on Amazon's very first day - to make smart, fast decisions, stay nimble, invent, and focus on delighting our customers.
Job ID: 106987979
Skills:
Text Analytics, Sql, Tensorflow, Nlp, Pytorch, Python, PEFT, Generative AI, Agentic AI Framework, Google Gemini, fine-tuning for pre-trained models, NLG, Data mining techniques, ML Ops, R, RAG Vector Database, LoRA, NLU, OpenAI, QLoRA
Skills:
Pyspark, Machine Learning, Exploratory Data Analysis, Artificial Intelligence, Big Data, Azure Databricks, Sql, Pig, Devops, Jenkins, Git, Hive, Docker, Spark, Python, Azure Pipelines, Statistical ML techniques, ML Ops, Kubeflow, MLFlow
Skills:
content management systems , Servicenow, BMC Remedy, JIRA, Confluence, Salesforce, DW BI AI ML, Cybersource, Zuora, Observability monitoring tools, ITIL principles, Atlassian Suite
Skills:
Apis, Quality assurance, Javascript, Python, LangChain, N8N, Prompt engineering strategies, Claude Code, AI coding tools, OpenAI, Orchestration platforms, AI pipeline components, Data pipeline development, Model evaluation, AI ML frameworks
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