AI/ML Architect, Retail and Marcom Engineering - Jobs - Careers at Apple
- Architectural Ownership: Define and own the end-to-end architecture for AI/ML systems, establishing standards, design patterns, and best practices across teams.
- GenAI System Architecture at Scale: Deep expertise architecting end-to-end GenAI pipelines (model selection, fine-tuning, RAG, guardrails), aggressively optimizing for latency, hallucination rates, and token costs at enterprise scale
- Platform & Systems Design: Architect scalable, resilient Conversational AI platforms and virtual assistant frameworks that serve as the foundation for multiple product experiences.
- Technical Strategy & Roadmap: Partner with business and engineering leadership to define the AI/ML architecture roadmap, identify strategic opportunities, and guide the adoption of LLM-driven analytic solutions across the organization.
- Technical Leadership: Mentor engineering teams on architectural decisions, review system designs, and ensure alignment with enterprise-wide technical standards.
- 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.
- 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.
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Required Skills
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🇬🇧 English