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About the Role

The iCloud Efficiency team is responsible for improving how Apple’s 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 efficiency. As 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 world’s largest private cloud environments supporting iCloud, Apple Intelligence, and Private Cloud Compute (PCC).

Responsibilities

Play a critical role in advancing Apple’s next generation of intelligent infrastructure operations through applied GenAI and agentic technologies.
Build practical, high-impact AI systems that improve engineering workflows and infrastructure decision-making.
Identify high-leverage operational problems.
Set architecture direction.
Design agentic solutions.
Guide teams from prototype to production adoption.
Combine LLM reasoning, system context, automation frameworks, and engineering safeguards to improve speed, reliability, and efficiency.
Define how AI can safely and effectively augment engineering teams—from capacity optimization and deployment analysis to incident response, forecasting, and infrastructure planning.

Requirements

5+ years of experience in software engineering, infrastructure engineering, or large-scale cloud services environments.
Proven experience designing, building, or technically leading production GenAI, ML platform, developer productivity, infrastructure automation, or tooling systems.
Hands-on experience with GenAI technologies, LLM application architecture, including retrieval, context engineering, tool use, workflow orchestration, agentic workflows, evaluation, observability, and failure handling.
Demonstrated technical leadership across teams, including architecture reviews, roadmap influence, mentoring, and driving adoption of shared engineering practices.
Strong understanding of cloud infrastructure operations, observability, deployment systems, and operational safety principles.
Proven ability to translate ambiguous operational challenges into practical engineering solutions with measurable business impact.
Strong software development skills in Python, Java, or similar languages.
Exceptional analytical, systems thinking, and cross-functional communication skills.

Skills

GenAI
LLM application architecture
Retrieval-assisted generation (RAG)
Skills-based automation
Agentic workflows
Evaluation and orchestration frameworks
Python
Java
Cloud infrastructure operations
Observability
Deployment systems
Operational safety principles
Capacity planning
Anomaly detection
Operational workflows
Deployment safety
Infrastructure optimization
Capacity forecasting
Cost optimization
Infrastructure efficiency modeling

Experience Level

Lead

Education Level

Bachelor’s or Master’s degree in Computer Science, Engineering, or related technical field.
PhD or advanced degree in Computer Science, Machine Learning, Distributed Systems, or related field.

About the Company

In Apple’s iCloud services organization, efficiency is not simply a technical objective—it 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 Apple’s cloud services utilize compute, storage, and operational resources at massive scale.
Your work will directly influence the operational and financial efficiency of one of the world’s largest private cloud environments supporting iCloud, Apple Intelligence, and Private Cloud Compute (PCC).