GenAI Architect, Global Retail & Marcom Systems

Apple, Inc.; Sunnyvale (CA); On-site; USD 216,000 - 325,000; Full time

Benefits offered by this job

Medical and dental coverage

Stock program eligibility

Relocation assistance

Education reimbursement

Employee discounts

Job summary

Apple is seeking an AI/ML Architect for Retail and Marcom Engineering in Sunnyvale, CA. You will shape the technical strategy for AI/ML systems spanning GenAI, Graph RAG, and multi-modal workflows that power Apple's brand experiences for customers worldwide.

You will own end-to-end system design, evaluate build vs. buy options, and guide multiple teams on best practices for scalable, reliable AI platforms. A strong track record in enterprise MLOps and governance is essential.

Qualifications

• 10+ years in software engineering or AI/ML
• 3–4+ years in an architect/leadership role for enterprise AI/ML
• Production experience with GenAI solutions and LLMs
• Experience designing prompt strategies at scale
• Experience with multi-agent systems and orchestration frameworks
• Experience integrating AI with backend systems and APIs

Responsibilities

• Define end-to-end AI/ML architecture across teams
• Architect GenAI pipelines focusing on latency and cost
• Design scalable Conversational AI platforms
• Define AI/ML strategy and roadmap with leadership
• Mentor teams on architectural decisions and standards

Skills

AI/ML system design

GenAI pipelines

LLM architectures

RAG pipelines

Multi-agent orchestration

AIOps & observability

Education

BS in Engineering/CS or equivalent

MS/PhD in related field

Tools

LangChain

LangGraph

AutoGen

Terraform

Docker

Job description

Apple is seeking an AI/ML Architect for Retail and Marcom Engineering in Sunnyvale, CA. You will shape the technical strategy for AI/ML systems spanning GenAI, Graph RAG, and multi-modal workflows that power Apple's brand experiences for customers worldwide.

You will own end-to-end system design, evaluate build vs. buy options, and guide multiple teams on best practices for scalable, reliable AI platforms. A strong track record in enterprise MLOps and governance is essential.