AI Data & Knowledge Engineer
Apple
- Location
- Onsite (Cupertino, California)
- Employment
- Full-time
- Level
- Senior Level
Posted 5 days ago
About the Role
Apple's US Sales Technology Team is seeking an AI Data & Knowledge Engineer to build data infrastructure and pipelines that support AI and machine learning initiatives for sales. This role focuses on automating sales processes and enabling the deployment of AIML models through scalable data solutions.
Skills
Knowledge graphs
RAG pipelines
Vector databases
SQL
Python
Java
R
Hadoop
Spark
Data pipelines
Git
CI/CD
Apache Airflow
Cloud platforms
Semantic layers
LLM evaluation
Full job details
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.
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.
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.
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.
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.