AI Data Platform Engineer

Job Title

AI Data Platform Engineer - Manufacturing Systems and Infrastructure

Job Summary

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 others 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. Its 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, youll do more than join something youll 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.

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.

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