AI/ML Architect, Retail and Marcom Engineering

Do you want to help build some of the largest and most consequential enterprise and customer technology systems in the world? Join Apple’s Information Systems and Technology (IS&T) organization. IS&T is the engine behind everything Apple does for customers and for the people who build for them. It’s Apple’s central nervous system. Supporting 2.5 billion active Apple devices, processing billions of secure transactions, and keeping the technology that defines modern life running flawlessly, IS&T makes the impossible feel effortless. Do you love building solutions to handle global complexity and immense scale? Imagine what you could do here.

Retail and Marcom Engineering, an IS&T team, builds and operates the systems and experiences that connect Apple's products with its customers. The team owns the technology behind both Apple's online and physical stores and drives the interactive marketing experiences and tools that keep creative operations moving. Together, those functions deliver the technology behind every product story Apple tells and every purchase a customer makes.

We're seeking a visionary AI/ML Architect to shape the technical strategy behind Apple's globally-led creative marketing team. You will define the architecture for cutting-edge AI/ML solutions, directly influencing how millions of Apple customers experience our brand. Lead the design of systems involving GenAI LLMs, Graph RAG, and multi-modal models, architecting intelligent agentic workflows that redefine what's possible. If you're ready to set the technical direction for AI/ML at a global scale, this is your opportunity.

As an AI/ML Architect, you'll define the technical vision and architecture for intelligent experiences, owning the end-to-end system design across the machine learning lifecycle. Partnering with business and engineering stakeholders, you'll define AI/ML architecture strategy, evaluate build vs. buy tradeoffs, and establish design patterns that leverage LLM technologies to unlock new insights at scale.

Your expertise will guide multiple teams on architectural best practices for LLM and GenAI usage, ensuring platform scalability, reliability, and long-term maintainability. You'll architect complex AIOps systems, establishing frameworks for clustering, classification, anomaly detection, and capacity prediction to optimize our systems.

Minimum Qualifications

10 years of experience in software engineering or AI/ML, including 3-4+ years in an architect or technical leadership role designing enterprise-scale AI/ML systems.
Understanding of core AI/ML concepts (supervised and unsupervised learning, deep learning, anomaly detection) and the ability to translate them into scalable system architectures.
Experience delivering production generative AI solutions, with working knowledge of LLM architectures, fine-tuning techniques, and RAG.
Experience designing and optimizing prompt strategies at scale, with measurable gains in LLM accuracy, latency, or cost-efficiency.
Experience architecting multi-agent systems using orchestration frameworks (e.g., LangChain, LangGraph, AutoGen), with a focus on rapid experimentation and continuous prompt and model iteration.
Experience designing integration architectures that connect AI and bot systems with backend systems, databases, and APIs to deliver robust end-to-end solutions.
Track record of designing, building, and scaling AI/ML platforms and services that handle high data and traffic volumes in enterprise environments.
Experience defining technical standards, conducting architecture reviews, and driving consistency across distributed engineering teams.
Troubleshooting and analytical skills, with the ability to diagnose and resolve complex issues in distributed environments.
BS in Engineering, Computer Science, or a related field, or equivalent experience.

Preferred Qualifications

MS or PhD in Engineering, Computer Science, or a related field.
Experience architecting solutions with multi-modal models and Graph RAG pipelines.
Experience establishing RAG benchmarking frameworks and evaluation methodologies.
Experience with model optimization techniques such as distillation, quantization, and parameter-efficient fine-tuning, and a record of quickly evaluating and prototyping emerging approaches (e.g., fine-tuning vs. RAG).
Experience architecting conversational AI platforms at scale, including intent recognition, dialogue management, and natural language generation.
Knowledge of DevOps practices, including CI/CD pipelines, infrastructure as code (e.g., Terraform), containerization (Docker), and Git-based workflows for automated build, test, and deployment of ML services.
Knowledge of SRE principles, including defining SLIs/SLOs, monitoring and alerting, incident response, root cause analysis, and building for reliability in production systems.
Experience architecting AIOps solutions, including clustering, classification, anomaly detection, and capacity prediction.
Experience with LLMOps, including evaluation, observability, guardrails, and cost and token optimization.
Familiarity with responsible AI practices, including privacy, brand safety, and governance.
Background in marketing, creative, content, or personalization technology.
Strategic, independent mindset with strong leadership instincts and the ability to thrive in a fast-paced, dynamic environment.