亚马逊Data Engineer, AWS GCR Tech

1. Generative AI Model Development: -Design and develop generative AI models, including language models, image generation models, and multimodal models. -Explore and implement advanced techniques in areas such as transformer architectures, attention mechanisms, and self-supervised learning. -Conduct research and stay up-to-date with the latest advancements in the field of generative AI. 2. Data Acquisition and Preprocessing: -Identify and acquire relevant data sources for training generative AI models. -Develop robust data preprocessing pipelines, ensuring data quality, cleanliness, and compliance with ethical and regulatory standards. -Implement techniques for data augmentation, denoising, and domain adaptation to enhance model performance. 3. Model Training and Optimization: -Design and implement efficient training pipelines for large-scale generative AI models. -Leverage distributed computing resources, such as GPUs and cloud platforms, for efficient model training. -Optimize model architectures, hyperparameters, and training strategies to achieve superior performance and generalization. 4. Model Evaluation and Deployment: -Develop comprehensive evaluation metrics and frameworks to assess the performance, safety, and bias of generative AI models. -Collaborate with cross-functional teams to ensure the successful deployment and integration of generative AI models into client solutions. 5. Collaboration and Knowledge Sharing: -Collaborate with data engineers, software engineers, and subject matter experts to develop innovative solutions leveraging generative AI. -Contribute to the firm's thought leadership by presenting at conferences, and participating in industry events.

• Design and implement end-to-end data pipelines (ETL) to ensure efficient data collection, cleansing, transformation, and storage, supporting both real-time and offline analytics needs. • Develop automated data monitoring tools and interactive dashboards to enhance business teams’ insights into core metrics (e.g., user behavior, AI model performance). • Collaborate with cross-functional teams (e.g., Product, Operations, Tech) to align data logic, integrate multi-source data (e.g., user behavior, transaction logs, AI outputs), and build a unified data layer. • Establish data standardization and governance policies to ensure consistency, accuracy, and compliance. • Provide structured data inputs for AI model training and inference (e.g., LLM applications, recommendation systems), optimizing feature engineering workflows. • Explore innovative AI-data integration use cases (e.g., embedding AI-generated insights into BI tools). • Provide technical guidance and best practice on data architecture and BI solution

1. Design, develop, and maintain scalable data pipelines to support ML model development and production deployment. 2. Implement and maintain CI/CD pipelines for the data and ML solutions. 3. Collaborate with data scientists and other team members to understand data requirements and implement efficient data processing solutions. 4. Create and manage data warehouses and data lakes, ensuring proper data governance and security measures are in place. 5. Collaborate with product managers and business stakeholders to understand data needs and translate them into technical requirements. 6. Stay current with emerging technologies and best practices in data engineering, and propose innovative solutions to improve data infrastructure and processes for ML models and analytics applications. 7. Participate in code reviews and contribute to the development of best practices for data engineering within the team.

Design and build cloud-based data warehouses to deliver efficient analytical and reporting capabilities for Apple’s global and regional sales and finance teams. Develop highly scalable data pipelines to ingest and process data from multiple source systems, leveraging Apache Airflow for workflow orchestration, scheduling, and monitoring. Architect generic, reusable solutions that enforce to data warehousing best practices while addressing complex business requirements. Analyze and optimize existing systems, providing improvements and ongoing support as needed. Uphold the highest standards of data integrity and software quality, ensuring reliable and accurate outputs. We are looking for a proactive self-starter who takes initiative, learns fast, and works well across teams. Join our growing team where no two days are the same - solving tough technical challenges and business problems in a fast-paced environment.