{"schemaVersion":"jobsearcher.job.v1","id":"7d7db12a2ff0ce08e35e5ce6","url":"https://jobsearcher.com/jobs/7d7db12a2ff0ce08e35e5ce6","canonicalUrl":"https://jobsearcher.com/jobs/7d7db12a2ff0ce08e35e5ce6","title":"AI Data & Knowledge Engineer","description":"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.\n\nApple’s Sales organization generates the revenue needed to fuel our ongoing development of products and services.\n\nApple'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.\n\nDescription\n\nAs 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.\",\"responsibilities\":\"Responsible for the development and design of data pipelines and data knowledge layers for agentic AI applications.\n\nDesign and implement data models for a semantic layer that integrates analytics data from multiple sources in an efficient and effective manner.\n\nDesign and build scalable data and knowledge layers that power chatbots and other agentic applications.\n\nBuild RAG-ready data pipelines and knowledge layers encompassing document ingestion, parsing, metadata tagging, embeddings, indexing with vector search.\n\nDesign scalable architecture for semantic and hybrid search, knowledge graphs to enable contextually accurate text-to-SQL generation.\n\nBuild mechanisms for incremental synchronization of data to knowledge updates so agent responses are current and reliable.\n\nDesigning and operating distributed data systems - from SQL/NoSQL databases, Vector search, and orchestration.\n\nCollaborate 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.\n\nCollaborate with internal business partners, internal technology resources (database, system, networking), external vendors, and partners.\n\nPlay an active role in the development and maintenance of user documentation, including data models, mapping rules, and data dictionaries.\n\nEnsure data quality and accuracy by developing data validation and reconciliation processes.\n\nBuild and maintain data pipelines for ingesting, processing, and transforming unstructured data sources, such as customer feedback, social media data, or sales call recordings.\n\nDevelop data quality monitoring and validation processes specifically for AIML datasets, including identifying and addressing data bias.\n\nWork with data scientists to understand data requirements for AIML model training and deployment, ensuring data is available in the appropriate format and quality.\n\nImplement data governance policies and procedures to ensure the responsible and ethical use of data in AIML applications.\n\nPreferred Qualifications\n\nExperience architecting and developing data pipelines through ETL tools, API integration with on-premise and cloud-based sources.\n\nExperience 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.\n\nStrong understanding of LLM evaluation and AI quality tooling, retrieval metrics, and observability to improve application reliability.\n\nExperience working with unstructured and Semi-structured data sets (e.g., JSON, Parquet, PDF, text, images, audio, video)\n\nExperience with data governance and observability tools; for example DataHub, Collibra\n\nExperience articulating and translating business questions into data solutions and proven ability to lead development projects from start to finish.\n\nBroad knowledge of web standards relating to REST, HTTP, JSON, etc.\n\nExperience with data labeling and annotation tools and processes.\n\nFamiliarity with AI/ML model development lifecycle and data needs for training and deployment.\n\nAbility to balance competing priorities, long-term projects, and ad hoc requirements.\n\nAbility to work in a fast-paced, dynamic, constantly evolving business environment.\n\nMinimum Qualifications\n\nExperience 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.\n\nExperience modeling enterprise knowledge and metadata within semantic layers to represent business entities, attributes, and their relationships.\n\n5+ years of experience in designing, building, and maintaining scalable data solutions for large-scale analytics.\n\nProficiency in SQL and development experience with cloud database environments like Snowflake, Redshift, Databricks.\n\nProficiency in programming languages like Python, Java, R and open-source frameworks for distributed processing like Hadoop and Spark.\n\nExperience building data pipelines to ingest, transform, and continuously synchronize structured and unstructured enterprise data from multiple sources.\n\nHands-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.\n\nExperience with Cloud platforms AWS, Azure or Google Cloud.\n\nPay & Benefits\n\nAt Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $149,200 and $249,000, and your base pay will depend on your skills, qualifications, experience, and location.\n\nApple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits\n\nNote: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.","company":"Apple","rawCompany":"apple","city":"Austin","state":"TX","isRemote":false,"isActive":true,"createdAt":"2026-10-16T14:14:17.958Z","occupations":[{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"AI Data & Knowledge Engineer","description":"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.\n\nApple’s Sales organization generates the revenue needed to fuel our ongoing development of products and services.\n\nApple'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.\n\nDescription\n\nAs 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.\",\"responsibilities\":\"Responsible for the development and design of data pipelines and data knowledge layers for agentic AI applications.\n\nDesign and implement data models for a semantic layer that integrates analytics data from multiple sources in an efficient and effective manner.\n\nDesign and build scalable data and knowledge layers that power chatbots and other agentic applications.\n\nBuild RAG-ready data pipelines and knowledge layers encompassing document ingestion, parsing, metadata tagging, embeddings, indexing with vector search.\n\nDesign scalable architecture for semantic and hybrid search, knowledge graphs to enable contextually accurate text-to-SQL generation.\n\nBuild mechanisms for incremental synchronization of data to knowledge updates so agent responses are current and reliable.\n\nDesigning and operating distributed data systems - from SQL/NoSQL databases, Vector search, and orchestration.\n\nCollaborate 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.\n\nCollaborate with internal business partners, internal technology resources (database, system, networking), external vendors, and partners.\n\nPlay an active role in the development and maintenance of user documentation, including data models, mapping rules, and data dictionaries.\n\nEnsure data quality and accuracy by developing data validation and reconciliation processes.\n\nBuild and maintain data pipelines for ingesting, processing, and transforming unstructured data sources, such as customer feedback, social media data, or sales call recordings.\n\nDevelop data quality monitoring and validation processes specifically for AIML datasets, including identifying and addressing data bias.\n\nWork with data scientists to understand data requirements for AIML model training and deployment, ensuring data is available in the appropriate format and quality.\n\nImplement data governance policies and procedures to ensure the responsible and ethical use of data in AIML applications.\n\nPreferred Qualifications\n\nExperience architecting and developing data pipelines through ETL tools, API integration with on-premise and cloud-based sources.\n\nExperience 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.\n\nStrong understanding of LLM evaluation and AI quality tooling, retrieval metrics, and observability to improve application reliability.\n\nExperience working with unstructured and Semi-structured data sets (e.g., JSON, Parquet, PDF, text, images, audio, video)\n\nExperience with data governance and observability tools; for example DataHub, Collibra\n\nExperience articulating and translating business questions into data solutions and proven ability to lead development projects from start to finish.\n\nBroad knowledge of web standards relating to REST, HTTP, JSON, etc.\n\nExperience with data labeling and annotation tools and processes.\n\nFamiliarity with AI/ML model development lifecycle and data needs for training and deployment.\n\nAbility to balance competing priorities, long-term projects, and ad hoc requirements.\n\nAbility to work in a fast-paced, dynamic, constantly evolving business environment.\n\nMinimum Qualifications\n\nExperience 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.\n\nExperience modeling enterprise knowledge and metadata within semantic layers to represent business entities, attributes, and their relationships.\n\n5+ years of experience in designing, building, and maintaining scalable data solutions for large-scale analytics.\n\nProficiency in SQL and development experience with cloud database environments like Snowflake, Redshift, Databricks.\n\nProficiency in programming languages like Python, Java, R and open-source frameworks for distributed processing like Hadoop and Spark.\n\nExperience building data pipelines to ingest, transform, and continuously synchronize structured and unstructured enterprise data from multiple sources.\n\nHands-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.\n\nExperience with Cloud platforms AWS, Azure or Google Cloud.\n\nPay & Benefits\n\nAt Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $149,200 and $249,000, and your base pay will depend on your skills, qualifications, experience, and location.\n\nApple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits\n\nNote: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.","datePosted":"2026-10-16T14:14:17.958Z","dateModified":"2026-10-16T14:14:17.958Z","hiringOrganization":{"@type":"Organization","name":"Apple","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Austin","addressRegion":"TX","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"7d7db12a2ff0ce08e35e5ce6"},"url":"https://jobsearcher.com/jobs/7d7db12a2ff0ce08e35e5ce6"}}