Principal AI Architect, App Store Data

Apple•Cupertino, CA

About The Position

Apple’s App Store is the world’s largest and most innovative app marketplace, home to over 1.5 million apps and serving more than half a billion customers every week across all Apple devices. Since the App Store launched in 2008, it has changed how we all live; it has enabled countless new companies, spawned new industries, and built millions of jobs. But we believe we are just getting started. Our team enables data-driven innovation for internal and external partners, while maintaining data quality, privacy, and engineering excellence as first-class citizens. We’re looking for a highly skilled hands-on Principal AI Architect with strong software development skills and a passion for applying LLMs and Agentic workflows to real-world business problems. In this role, you will design and ship AI systems that make App Store data useful, safe, and actionable — from agentic workflows that automate analytical tasks, to LLM-powered data products that surface insights. The ideal candidate is someone who moves fluidly from prototype to production, writes the code they architect, and takes personal ownership of the systems they ship. They combine deep expertise in AI and distributed systems with strong product instincts, excellent communication skills, and genuine curiosity about data analytics workflows, and they know when to use AI and when deterministic logic is the better answer. This is a high-leverage role at the intersection of data engineering, AI, product impact and technical leadership.

Requirements

Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent experience).
Proven ability to translate business needs and ideas into scalable, production-level AI solutions.
Proven ability to communicate complex technical concepts to non-technical stakeholders.
Extensive experience with LLMs and agentic workflows, MCP and A2A protocols, prompt engineering.
Experience building complex agentic AI systems using LangChain, LangGraph, Claude Agent SDK, or similar AI agent frameworks.
12+ years of software engineering experience with 5+ years of experience building and deploying AI/ML solutions in production environments.
Hands-on industry experience shipping LLM-powered products.
Knowledge of "Prompt Injection"/ Guardrail defense and secure context handling (PII redaction).
Experience with privacy regulations and compliance obligations (GDPR, CCPA, DMA, DSA) and their impact on AI system design and data architecture.
Experience in designing scalable, highly available distributed systems which can handle high data volumes.

Responsibilities

Translate business problems and objectives into actionable AI initiatives; identify opportunities where AI can add value and create roadmaps for implementation with clear milestones and dependencies.
Guide the organizational’s AI strategy, technology stack, and framework selection.
Design production-grade scalable, secure, and efficient AI systems that reason, plan, and act across tools and modalities for data pipelines and applications.
Establish enterprise AI reference architectures and reusable components covering data flows, model lifecycle, runtime patterns and integration approaches.
Monitor emerging trends in AI and conduct proof-of-concepts (PoCs) to evaluate new technologies.
Work closely with engineers and partner teams to build new AI-enabled data pipelines and applications.
Evaluate and select the most suitable tools, platforms, and technologies for AI development, considering factors like scalability, cost, and compatibility.
Continuously assess the performance of AI systems (quality, latency, drift, safety, and business impact).
Work on problems in an agentic context: distributed state persistence, message consensus between agents (A2A), and handling non-deterministic failures in long-running loops, context engineering, agentic memory.
Ensure AI solutions adhere to security and compliance policies, collaborate with Security, Privacy and Legal teams to apply existing or develop new AI policies, including sensitive data handling.
Establish and enforce AI governance: architecture review criteria, documentation standards, approval procedures, safety filtering, and human-in-the-loop mechanisms.
Apply responsible AI principles including bias assessment and explainability where appropriate.
Define AI engineering standards and best practices; guide engineers on AI use cases, design decisions, risks and trade-offs.
Raise the organization's AI maturity through mentorship, communities of practice, and influence rather than authority.