{"id":2131925,"url":"https://alion.io/job/apple-ai-data-infrastructure-engineer-2","title":"AI Data & Infrastructure Engineer","company":{"id":12,"name":"Apple","domain":"apple.com","url":"https://alion.io/company/apple","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Apple Jobs","truth_index":{"grade":"B","score":80,"open_postings":985,"ghost_share":0,"stale_share":0.805,"repost_share":0,"time_to_fill_p50_days":47,"computed_at":"2026-10-16T05:45:00Z"}},"role":"DevOps","role_family":"DevOps","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Sunnyvale, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":180000,"max_usd":401000,"period":"year","method":"role_country_seniority_unknown","sample_n":3941},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Delta Lake","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Java","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"MLFlow","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Pandas","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Ray","optional":false},{"name":"Scala","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-09-11T15:48:07Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-16T22:23:12Z","board_verified":true,"closed_at":null,"days_open":35,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":35},"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.\nThe 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.\nManufacturing 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.\nAs 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.\nDescription\nCreate robust, scalable architectures for systems that handle data orchestration for AI features\nDesign, build, and maintain scalable AI data platforms, services, and APIs that support and enable Agentic AI workflow development & automation.\nDevelop data ingestion, transformation, and publishing pipelines for structured, unstructured, and multimodal data.\nDesign and implement Retrieval-Augmented Generation (RAG) pipelines, embedding workflows, vector database integrations, and metadata services for enterprise AI applications.\nBe 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.\nBuild and integrate tools that help make complex AI systems observable, understandable and debuggable\nStrong understanding of distributed systems, parallel computing, and performance optimization\nCollaborate with AI/ML engineers, software engineers, product teams, and domain experts to define AI data requirements and deliver production-ready data solutions.\nOptimize platform scalability, reliability, performance, security, and cost across cloud-native environments and Agentic systems.\nAbility to clearly communicate complex technical problems and collaborate with partners to develop solutions\nMinimum Qualifications\nBachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, or a related field.\n8+ Experience designing and building scalable data platforms and distributed systems.\nStrong programming skills in Python and SQL, with proficiency in Java or Scala preferred.\nExperience with Airflow, Kubeflow, or MLflow to build and orchestrate scalable AI data pipelines.\nExperience 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).\nExperience using modern development tools, including AI-assisted coding tools, while applying sound engineering judgment to review, validate, and improve generated code.\nKnowledge of RAG architectures, embedding generation, vector databases, and AI data preparation for LLMs and agentic AI.\nExperience with cloud platforms (AWS, Azure, or GCP), Kubernetes, Docker, CI/CD, and Infrastructure as Code.\nDemonstrated ability to understand complex user workflows, translate them into practical technical solutions, and collaborate across teams to deliver measurable outcomes.\nExcellent communication and collaboration skills, with the ability to translate technical concepts into clear, business focused 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