AI Data & Engineer

Cupertino, California, United States Sales and Business Development

Imagine what you could do here. At Apple, new ideas have a way of becoming outstanding products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish.

Apple’s Sales organization generates the revenue needed to fuel our ongoing development of products and services.

Apple's US Sales Technology Team is looking for a talented individual who is passionate about crafting, implementing, and operating solutions that have a direct and measurable impact on Apple Sales and its customers. We also leverage Artificial Intelligence and Machine Learning (AIML) to enhance our sales processes, and this role will be critical in building the data infrastructure to support those initiatives.

As an AI Data & Knowledge Engineer, you will develop infrastructure, systems, services, and tools for automating sales processes. We’re looking for an exceptional engineer that lives at the intersection of development, operations, data, and systems engineering to build solutions for large-scale continuous data transformation and delivery. This role will specifically focus on building and maintaining data pipelines for both structured and unstructured data, enabling the development and deployment of AIML models.

• Responsible for the development and design of data pipelines and data knowledge layers for agentic AI applications.
• Design and implement data models for a semantic layer that integrates analytics data from multiple sources in an efficient and effective manner.
• Design and buildscalable data and knowledge layers that power chatbots and other agentic applications.
• BuildRAG-ready data pipelines and knowledge layers encompassing document ingestion, parsing, metadata tagging, embeddings, indexing with vector search.
• Design scalable architecture for semantic and hybrid search, knowledge graphs to enable contextually accurate text-to-SQL generation.
• Build mechanisms for incremental synchronization of data to knowledge updatesso agent responses are current and reliable.
• Designing and operating distributed data systems — from SQL/No
  
  SQL databases, Vector search, and orchestration.
• Collaborate with Analytics and Data Science teamsto translate business requirements into reliable, actionable knowledge layers that support AI agent development and deliver targeted business outcomes.
• Collaborate with internal business partners, internal technology resources (database, system, networking), external vendors, and partners.
• Play an active role in the development and maintenance of user documentation, including data models, mapping rules, and data dictionaries.
• Ensure data quality and accuracy by developing data validation and reconciliation processes.
• Build and maintain data pipelines for ingesting, processing, and transforming unstructured data sources, such as customer feedback, social media data, or sales call recordings.
• Develop data quality monitoring and validation processes specifically for AIML datasets, including identifying and addressing data bias.
• Work with data scientists to understand data requirements for AIML model training and deployment, ensuring data is available in the appropriate format and quality.
• Implement data governance policies and procedures to ensure the responsible and ethical use of data in AIML applications.

• Experience designing and building knowledge layers for AI systems, including knowledge graphs, RAG pipelines, and vector databases to ground LLM-driven applications in accurate, structured, unstructured and retrievable enterprise knowledge.
• Experience modeling enterprise knowledge and metadata within semantic layers to…