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AI Data & Knowledge Engineer

Apple· Sales and Business Development
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Hybrid (Austin, Texas, United States), Hybrid (Cupertino, California, United States)Full-timePosted 1w ago

About the role

Role within Apple’s US Sales Technology team focusing on building data infrastructure, pipelines, and knowledge layers to support AIML-enabled sales processes and agentic AI applications.

Responsibilities

  • 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 build scalable data and knowledge layers that power chatbots and other agentic applications.
  • Build RAG-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 updates so agent responses are current and reliable.
  • Designing and operating distributed data systems — from SQL/NoSQL databases, Vector search, and orchestration.
  • Collaborate with Analytics and Data Science teams to 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.

Qualifications

  • 5+ years of experience in designing, building, and maintaining scalable data solutions for large-scale analytics.
  • Proficiency in SQL and development experience with cloud database environments like Snowflake, Redshift, Databricks.
  • Proficiency in programming languages like Python, Java, R and open-source frameworks for distributed processing like Hadoop and Spark.
  • Experience building data pipelines to ingest, transform, and continuously synchronize structured and unstructured enterprise data from multiple sources.
  • Hands-on experience using development tools in a modern cloud data stack for code management, versioning using Git, CI/CD tools, automation and orchestration using Apache Airflow or others and monitoring & alerting.
  • Experience with Cloud platforms AWS, Azure or Google Cloud.
  • Experience architecting and developing data pipelines through ETL tools, API integration with on-premise and cloud-based sources.
  • Experience building ontology-based semantic layer including a business ontology of sales concepts, a technical ontology of data sources and schemas, and execution traces that provide feedback for continuous improvement.
  • Strong understanding of LLM evaluation and AI quality tooling, retrieval metrics, and observability to improve application reliability.
  • Experience working with unstructured and Semi-structured data sets (e.g., JSON, Parquet, PDF, text, images, audio, video)
  • Experience with data governance and observability tools; for example DataHub, Collibra
  • Experience articulating and translating business questions into data solutions and proven ability to lead development projects from start to finish.
  • Broad knowledge of web standards relating to REST, HTTP, JSON, etc.
  • Experience with data labeling and annotation tools and processes.
  • Familiarity with AI/ML model development lifecycle and data needs for training and deployment.
  • Ability to balance competing priorities, long-term projects, and ad hoc requirements.
  • Ability to work in a fast-paced, dynamic, constantly evolving business environment.

Skills

Required:Data pipeline design and constructionSemantic data modelingRAG pipelines and vector databasesSQLCloud platforms (AWS/Azure/Google Cloud)Python/Java/RETL tools and API integrationData governance and observability
Nice to have:LLM evaluation and AI quality toolingData labeling and annotation tools
Last updated Oct 16, 2026. Interested? Apply on the company site.

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