AI Data & Knowledge Engineer

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.

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.