AI Data Platform Engineer - Manufacturing Systems and Infrastructure

Apple

About The Position

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other's ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It's the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you'll do more than join something - you'll add something. Manufacturing Systems and Infrastructure (MSI) team is an engineering organisation under the Product Operations org. MSI is responsible for the design, development and maintenance of system tools, services and applications required to efficiently run manufacturing operations at scale across global factory sites. As an AI Data Platform Engineer with the MSI team, you will design, build, and operate scalable AI data platforms that enable GenAI, Agentic AI, and Embodied AI solutions across the enterprise. You will develop reusable platform services, data pipelines, and data quality frameworks that transform fragmented enterprise and multimodal data into trusted, AI-ready datasets — combining expertise in AI data platform engineering, data quality, systems engineering, and AI data lifecycle management to accelerate AI innovation.

Requirements

5+ years of 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)
Hands-on experience with AI data engineering, including ground truth dataset creation, data curation, annotation pipelines, dataset versioning, and metadata management
Bachelors / Masters in Computer Science or related fields
Experience implementing data validation, quality frameworks, observability, and AI dataset evaluation
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
Strong understanding of distributed systems, APIs, microservices, and enterprise integration patterns
Excellent communication, collaboration, and technical leadership skills

Nice To Haves

Experience building platforms supporting GenAI, Agentic AI, or Embodied AI applications
Experience with multimodal datasets, knowledge graphs, AI evaluation frameworks, or vector search technologies
Familiarity with enterprise data governance, lineage, metadata management, and AI compliance
Experience working with manufacturing, operational, IoT, or industrial data platforms
Demonstrated ability to lead technical initiatives and mentor engineers

Responsibilities

Design, build and maintain scalable AI data platforms, services and APIs that support and enable AI model development and production.
Develop data ingestion, transformation and publishing pipelines for structured, unstructured and multimodal data.
Build AI-ready datasets through ground truth creation, data curation, annotation workflows, dataset versioning, and metadata management.
Develop data quality frameworks, validation pipelines, observability and evaluation metrics to ensure trusted AI datasets.
Design and implement Retrieval-Augmented Generation (RAG) pipelines, embedding workflows, vector database integrations and metadata services for enterprise AI applications.
Build scalable platform capabilities for managing the end-to-end AI data lifecycle, including ground truth dataset creation, dataset versioning, metadata and lineage management, automated data quality validation, governance, and secure publishing of AI-ready datasets.
Collaborate with AI/ML engineers, software engineers, product teams, and domain experts to define AI data requirements and deliver production-ready data solutions.
Optimise platform scalability, reliability, performance, security, and cost across cloud-native environments.
Drive engineering best practices for AI data architecture, platform design, automation, testing, monitoring, and operational excellence.
Evaluate emerging AI technologies and continuously improve platform capabilities that enable GenAI, agentic AI, and embodied AI solutions.