Applied AI & Data Engineer - Business & Education

Applied AI & Data Engineer - Business & Education

Cupertino, California, United States Software and Services

Would you like to help shape how AI and modern data engineering & analytics come together to power Apple's Business and Education products, at a scale that touches millions of enterprises, schools, and the devices and services they depend on?

Apple's Business and Education organization builds the infrastructure, platforms, and services behind Apple's offerings for enterprise and education customers: device management, identity, and subscription services, and the classroom apps built on top of them. Our team owns the data engineering behind all of it, from pipelines and lakehouse architecture through analytics reporting, and we are building a new generation of AI-native capabilities on top: agents, intelligent workflows, and self-serve analytics that change how our Data Engineering, Analytics, and Data Science teams work.

The ambition is to define what an AI-first data organization looks like at Apple scale.

We are looking for a well-rounded builder. You spent the earlier part of your career deep in software, data, or ML engineering, and the last few years applying that foundation to ship Applied AI products end-to-end. You think architecturally, you know where LLMs and agents earn their keep and where deterministic code is the better answer, and you would rather measure a system's quality than argue about it.

• Design and build the data and AI platform for Business and Education on Databricks, AWS, and modern cloud-native patterns, along with the data models and data-quality practices that make it trustworthy.
• Own AI and agentic systems end-to-end: retrieval, planning, evaluation, guardrails, responsible-AI review, deployment, and the on-call rotation that keeps them working.
• Build the data foundation for GenAI, agentic AI, and advanced analytics: RAG pipelines, vector search, knowledge graphs, and multi-agent orchestration, so the organization can ship natural-language data interfaces, AI agents, tool-calling workflows, and data-driven web apps.
• Keep those systems efficient enough to scale, through model selection and serving decisions, latency and throughput work, and token economics at Apple volume.
• Partner with product, business, analytics, and AI stakeholders to turn ambiguous requirements into secure, scalable, production-ready systems.
• Provide hands-on technical leadership through design reviews, implementation guidance, and production-readiness checks, and own projects across their full lifecycle, from discovery and planning through rollout.
• Mentor engineers, prioritize and resource across concurrent initiatives, and help the team adopt AI-native practices as they emerge, through workshops, technical playbooks, and design guidance.
• Explore state-of-the-art data and AI techniques, including agentic patterns, evaluation methods, AI-native developer tools, and modern data architectures, and turn them into capabilities that make our Data Engineering, Analytics, and Data Science teams measurably faster: AI-accelerated pipeline development, intelligent alerting, and natural-language access to data.

• 8+ years across data engineering, analytics engineering, software engineering, or ML engineering, with the last 3+ years building and shipping Applied AI and agentic LLM systems in production. You are still a builder: you want to spend real time writing code, prototyping, and shipping alongside your team, not only reviewing what others ship.
• You architect, build, and operate production AI products composed of LLMs, foundation models, agents, and deterministic components, for both human and machine consumers. You have clear judgment on where to infer and where to compute, how to decompose tasks across specialized models, how to orchestrate multi-step reasoning and tool use, and how the system degrades when a model fails.
• Hands-on fluency with modern LLM and agent frameworks (Lang Chain, Llama Index, Semantic Kernel, Google ADK, or equivalent), vector search (pgvector, FAISS, Pinecone, or equivalent), RAG pipelines, multi-agent coordination, tool…