AI Data & Infrastructure Engineer

Socket.dev; Sunnyvale (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Apple is seeking an AI Data & Infrastructure Engineer within the Manufacturing Systems and Infrastructure (MSI) group to design, build, and operate scalable AI data systems powering GenAI and Agentic AI capabilities across global factory sites.

You will develop data pipelines, embeddings, and retrieval augmented generation workflows while collaborating with AI/ML engineers, software teams, and domain experts to deliver production-ready data solutions.

Qualifications

• Bachelor's or Master's in Computer Science, Software Engineering, Data Engineering, or related field.
• 8+ years designing and building scalable data platforms and distributed systems.
• Strong Python and SQL skills; Java or Scala preferred.
• Experience with Airflow, Kubeflow, or MLflow for orchestrating AI data pipelines.
• Experience building batch and streaming data pipelines with Spark, Kafka, Airflow, and Ray; Pandas and modern data lake architectures (Iceberg/Delta Lake).
• Familiarity with AI-assisted coding tools and sound engineering judgment on generated code.
• Knowledge of RAG architectures, embedding generation, vector databases, and AI data prep for LLMs/agentic AI.
• Experience with AWS/Azure/GCP, Kubernetes, Docker, CI/CD, and infrastructure as code.
• Ability to translate complex user workflows into practical technical solutions and collaborate across teams.
• Excellent communication and collaboration skills to deliver measurable outcomes.

Responsibilities

• Create robust, scalable architectures for data orchestration in AI features.
• Design, build, and maintain scalable AI data platforms, services, and APIs for Agentic AI workflows.
• Develop data ingestion, transformation, and publishing pipelines for structured and unstructured data.
• Design and implement RAG pipelines, embedding workflows, vector DBs, and metadata services for enterprise AI apps.
• Build tools to make complex AI systems observable, debuggable, and understandable.
• Collaborate with AI/ML engineers, software groups, and domain experts to define data requirements.
• Optimize platform scalability, reliability, performance, security, and cost across cloud-native environments.

Skills

Communication skills

Team collaboration

Education

Bachelor's or Master's in CS/Software/Data Engineering

Tools

Python

SQL

Java/Scala

Airflow

Kubeflow

MLflow

Spark / PySpark

Kafka

Pandas

Iceberg / Delta Lake

Cloud: AWS/Azure/GCP

Kubernetes

Docker

CI/CD

Infra as Code

RAG / Vector DBs

LLMs / Agentic AI

Job description

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 systemsExperience 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.