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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Data Platform Engineer - **Company:** Apple Inc. - **Location:** Cupertino, CA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Computing Platforms, Microsoft Azure, Cloud Computing, Computer Programming, Continuous Integration, Data Architecture, Data Validation, Data Cleansing, Information Engineering, Data Governance, Data Infrastructure, Data Systems, Distributed Systems, Graph Database, Python (Programming Language), Meta-Data Management, Search Technologies, Software Engineering, SQL Databases, Data Streaming, Management of Software Versions, Enterprise Application Integration, Data Ingestion, Large Language Models, Apache Spark, Generative AI, Pandas, Data Lakes, Pyspark, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Apache Kafka, Data Management, Machine Learning Operations, Virtual Agents, Data Pipelines, Docker, Microservices - **Published:** August 2, 2026 - **Apply:** https://www.techcareers.com/job.asp?id=3339358233&tx=JL9993FFR&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54 ## About the Role * Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or a related field. * 5+ 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. * 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., * 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. ## Description 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., 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. Optimize 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. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Developer Experience, Platform Engineering and AI powered Apps](https://www.wearedevelopers.com/videos/990-developer-experience-platform-engineering-and-ai-powered-apps) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Got AI ideas but no money? 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