AI/ML Architect, Retail and Marcom Engineering

Apple • Sunnyvale, CA

7h

About The Position

Apple's Retail and Marcom Engineering team is seeking an AI/ML Architect to shape the technical strategy for its globally-led creative marketing team. The role defines architecture for AI/ML solutions, including systems using generative AI and large language models (LLMs), Graph RAG, and multimodal models, and designs agentic workflows that influence customer experiences at global scale.

The architect owns end-to-end system design across the machine learning lifecycle and partners with business and engineering stakeholders to set AI/ML architecture strategy, assess build-versus-buy tradeoffs, and establish design patterns for LLM technologies. The role guides multiple teams on LLM and generative AI architectural practices, with a focus on scalability, reliability, and maintainability. It also architects AIOps systems and frameworks for clustering, classification, anomaly detection, and capacity prediction.

Requirements

• At least 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 supervised and unsupervised learning, deep learning, and anomaly detection, with the ability to translate these concepts into scalable system architectures.
• Experience delivering production generative AI solutions and 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 with orchestration frameworks such as LangChain, LangGraph, or AutoGen, supporting rapid experimentation and continuous prompt and model iteration.
• Experience designing integration architectures that connect AI and bot systems with backend systems, databases, and APIs.
• Track record of designing, building, and scaling AI/ML platforms and services for 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, including diagnosing and resolving complex issues in distributed environments.
• BS in Engineering, Computer Science, or a related field, or equivalent experience.

Nice To Haves

• MS or PhD in Engineering, Computer Science, or a related field.
• Experience architecting solutions with multimodal 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 quickly evaluating and prototyping emerging approaches such as fine-tuning versus 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 such as Terraform, Docker, and Git-based workflows for automated ML-service build, test, and deployment.
• Knowledge of SRE principles, including SLIs/SLOs, monitoring and alerting, incident response, root cause analysis, and production reliability.
• Experience architecting AIOps solutions involving 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 and independent mindset, strong leadership instincts, and ability to thrive in a fast-paced, dynamic environment.

Responsibilities

• Define the technical vision and architecture for intelligent AI/ML experiences.
• Own end-to-end system design across the machine learning lifecycle.
• Partner with business and engineering stakeholders to establish AI/ML architecture strategy.
• Evaluate build-versus-buy tradeoffs and establish design patterns for LLM technologies.
• Guide multiple teams on architectural best practices for LLM and generative AI usage.
• Ensure platform scalability, reliability, and long-term maintainability.
• Architect AIOps systems and frameworks for clustering, classification, anomaly detection, and capacity prediction.
• Define technical standards, conduct architecture reviews, and drive consistency across distributed engineering teams.
• Design integration architectures connecting AI and bot systems with backend systems, databases, and APIs.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior