AI Data & Infrastructure Engineer

Imagine what you could do here. At Apple, we believe new insights have a way of

becoming excellent products, services, and customer experiences very quickly.

Bring passion and dedication to your job and there’s no telling what you could

accomplish. The people here at Apple don’t just build products — they build the

kind of wonder that’s revolutionized entire industries. It’s the diversity of

those people and their ideas that inspires the innovation that runs through

everything we do, from amazing technology to industry-leading environmental

efforts. Join Apple, and help us leave the world better than we found it.

Manufacturing Systems and Infrastructure (MSI) team is an engineering

organization under the Product Operations org. MSI is responsible for the

design, development, and maintenance of systems tools, services, and

applications required to efficiently run manufacturing operations at scale

across global factory sites. As an AI Data & Infrastructure Engineer with the

MSI team, you will won the end to end design, build, and operation of scalable

AI data systems that power enterprise GenAI and Agentic AI capabilities. Your

work spans core platform services, data pipeline development and infrastructure

provisioning, enabling manufacturing workflow automation through Agents and

Agent skills.

DESCRIPTION

Create robust, scalable architectures for systems that handle data orchestration

for AI features Design, build, and maintain scalable AI data platforms,

services, and APIs that support and enable Agentic AI workflow development &

automation. Develop data ingestion, transformation, and publishing pipelines for

structured, unstructured, and multimodal data. Design and implement

Retrieval-Augmented Generation (RAG) pipelines, embedding workflows, vector

database integrations, and metadata services for enterprise AI applications. Be

able to quickly build an idea so you and the team can work with hands-on

products. Then iterate on the best of those prototypes. Build and integrate

tools that help make complex AI systems observable, understandable and

debuggable Strong understanding of distributed systems, parallel computing, and

performance optimization Collaborate with AI/ML engineers, software engineers,

product teams, and domain experts to define AI data requirements and deliver

production-ready data solutions. Optimize platform scalability, reliability,

performance, security, and cost across cloud-native environments and Agentic

systems. Ability to clearly communicate complex technical problems and

collaborate with partners to develop solutions

MINIMUM QUALIFICATIONS

Bachelor's or Master's degree in Computer Science, Software Engineering, Data

Engineering, or a related field. 8+ Experience designing and building scalable

data platforms and distributed systems. Strong programming skills in Python and

SQL, with proficiency in Java or Scala preferred. Experience with Airflow,

Kubeflow, or MLflow to build and orchestrate scalable AI data pipelines.

Experience building scalable batch and streaming data pipelines using Spark

(PySpark), Kafka, Airflow, and Ray, with proficiency in Pandas and modern data

lake/lakehouse architectures (e.g., Iceberg, Delta Lake). Experience using

modern development tools, including AI-assisted coding tools, while applying

sound engineering judgment to review, validate, and improve generated code.

Knowledge of RAG architectures, embedding generation, vector databases, and AI

data preparation for LLMs and agentic AI. Experience with cloud platforms (AWS,

Azure, or GCP), Kubernetes, Docker, CI/CD, and Infrastructure as Code.

Demonstrated ability to understand complex user workflows, translate them into

practical technical solutions, and collaborate across teams to deliver

measurable outcomes. Excellent communication and collaboration skills, with the

ability to translate technical concepts into clear, business focused insights.

PREFERRED QUALIFICATIONS

Experience with or a strong understanding of Generative AI, LLMs, Agentic

Systems, or RAG (Retrieval-Augmented Generation) workflows. Experience

integrating LLMs into existing systems Experience with API design, both for

other engineers to use, but also for AI systems. Experience working with

manufacturing, operational, IoT, or industrial data platforms. Demonstrated

ability to lead technical initiatives and mentor engineers.