Lead AIML Engineer (GenAI & Agentic Systems) iCloud

In Apples iCloud services organization efficiency is not simply a technical objectiveit is a fundamental part of how we deliver reliable scalable and sustainable infrastructure for billions of users worldwide. The iCloud Efficiency team is responsible for improving how Apples cloud services utilize compute storage and operational resources at massive scale. As infrastructure complexity grows the opportunity to apply Generative AI intelligent automation and agentic systems becomes increasingly critical to accelerating operational excellence improving engineering productivity and optimizing resource a Lead AI/ML Engineer focused on GenAI and Agentic Systems you will work at the intersection of large-scale systems engineering infrastructure automation AI-assisted operations and intelligent decision systems. You will provide technical leadership for streamlining GenAI efforts across the organization: establishing reusable patterns defining production standards and helping teams converge on durable safe and measurable AI-assisted infrastructure workflows. You will apply production state-of-the-art LLM systems retrieval-assisted generation (RAG) skills-based automation agentic workflows evaluation and orchestration frameworks to transform how engineering teams operate troubleshoot forecast and optimize cloud infrastructure. This role involves partnering closely with data engineering data science infrastructure engineering software reliability engineering and finance teams to design and deploy AI-driven systems that improve efficiency across capacity planning anomaly detection operational workflows deployment safety and infrastructure optimization. Your work will directly influence the operational and financial efficiency of one of the worlds largest private cloud environments supporting iCloud Apple Intelligence and Private Cloud Compute (PCC).n

The Lead AI/ML Engineer will play a critical role in advancing Apples next generation of intelligent infrastructure operations through applied GenAI and agentic technologies. This role focuses on building practical high-impact AI systems that improve engineering workflows and infrastructure decision-making. You will identify high-leverage operational problems set architecture direction design agentic solutions and guide teams from prototype to production adoption. The goal is combining LLM reasoning system context automation frameworks and engineering safeguards to improve speed reliability and efficiency. Success in this role will be measured by concrete outcomes: adoption of shared patterns and tools by multiple teams measurable toil reduction validated cost or capacity savings. You will help define how AI can safely and effectively augment engineering teamsfrom capacity optimization and deployment analysis to incident response forecasting and infrastructure planning.

Design and implement GenAI-powered solutions to improve infrastructure efficiency operational workflows and engineering productivity across iCloud servicesnBuild and deploy agentic systems using technologies such as Claude LLM orchestration frameworks skills-based execution models and intelligent automation pipelinesnDevelop AI-assisted workflows for capacity planning anomaly detection forecasting deployment validation and operational safetynPartner with SRE infrastructure engineering platform teams and finance to identify high-value efficiency opportunities and convert them into scalable AI solutionsnCreate reliable and safe agent workflows with strong observability guardrails human-in-the-loop validation and operational controlsnImprove engineering decision-making by combining infrastructure telemetry operational context and LLM reasoning into actionable recommendationsnDrive experimentation and adoption of AI-first engineering practices that reduce toil improve reliability and optimize cost structuresnContribute to the long-term strategy for AI-assisted infrastructure operations across Apple Cloud Services

5 years of experience in software engineering infrastructure engineering or large-scale cloud services environmentsnProven experience designing building or technically leading production GenAI ML platform developer productivity infrastructure automation or tooling systemsnHands-on experience with GenAI technologies LLM application architecture including retrieval context engineering tool use workflow orchestration agentic workflows evaluation observability and failure handlingnDemonstrated technical leadership across teams including architecture reviews roadmap influence mentoring and driving adoption of shared engineering practicesnStrong understanding of cloud infrastructure operations observability deployment systems and operational safety principlesnProven ability to translate ambiguous operational challenges into practical engineering solutions with measurable business impactnStrong software development skills in Python Java or similar languagesnExceptional analytical systems thinking and cross-functional communication skillsnBachelors or Masters degree in Computer Science Engineering or related technical fieldn

Experience applying GenAI to infrastructure operations SRE workflows capacity planning or engineering productivity systemsnExperience building AI systems with operational guardrails governance models and safe deployment patterns for enterprise environmentsnStrong understanding of capacity forecasting cost optimization and infrastructure efficiency modeling at hyperscalenBackground working in private cloud environments large-scale storage systems or global distributed infrastructurenPhD or advanced degree in Computer Science Machine Learning Distributed Systems or related field