AI Data & Knowledge Engineer

Apple Inc.; Austin (TX); On-site; USD 140,000 - 190,000; Full time

Job summary

Apple Inc. Austin is seeking an AI Data & Knowledge Engineer to build robust data pipelines and semantic layers powering agentic AI applications. You will design scalable architectures, from SQL/NoSQL to vector search, to support AIML models and emerging sales AI tools.

You will partner with Analytics, Data Science, and business teams to ensure data quality, governance, and reliable data delivery for AI-driven sales initiatives.

Qualifications

• 5+ years designing, building scalable data solutions for analytics.
• Proficiency in SQL and cloud data platforms (Snowflake, Redshift, Databricks).
• Proficiency in Python, Java, R; experience with Hadoop/Spark.

Responsibilities

• Develop data pipelines and knowledge layers for AI applications.
• Design data models for semantic layers integrating analytics data.
• Build scalable data and knowledge layers powering chatbots and AI agents.
• Collaborate with Analytics and Data Science to translate requirements into reliable data solutions.

Skills

Knowledge graphs

RAG pipelines

Vector databases

SQL

Python

Java

Spark

Airflow

Cloud platforms

Tools

Snowflake

Redshift

Databricks

Git

CI/CD

Apache Airflow

Job description

Austin, Texas, 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.

Description

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.

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 buildscalable data and knowledge layersthat power chatbots and other agentic applications.
• BuildRAG-ready data pipelines and knowledge layersencompassing document ingestion, parsing, metadata tagging, embeddings, indexing with vector search.
• Design scalable architecture forsemantic and hybrid search, knowledge graphs to enable contextually accurate text-to-SQL generation.
• Build mechanisms for incremental synchronization of data to knowledge updatesto agent responses are current and reliable.
• Designing and operating distributed data systems - from SQL/NoSQL databases, Vector search, and orchestration.
• Collaborate withAnalytics and Data Science teamsto translate business requirements intoreliable, actionable knowledge layersthat 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.

Minimum Qualifications

• 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 represent business entities, attributes, and their relationships.
• 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.

Preferred Qualifications

• 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.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant

At Apple, we believe accessibility is a fundamental human right. You'll find that idea reflected in everything here - in our culture, our benefits and our digital tools. By welcoming as many perspectives as possible, we help you build a career where you feel like you belong. Learn about accessibility in Apple's workplace Learn about reasonable accommodations for job applicants