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Apple

Designs phones, computers and their operating systems

Systems Architect - Retail, Marcom Engineering

Cloud EngineeringConfirmed live in the last 24 hours
Full-timeAustin, TX, USANo salary listedIn-person

About the Job

Requirements

  • A bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience.
  • At least 12 years of hands-on experience in infrastructure, DevOps, platform, or software engineering, including at least 3 years in a senior role.
  • Expertise in AWS and Google Cloud Platform, including network topology, identity and access management, cost governance, and multi-account strategy.
  • Proficiency in Docker, Kubernetes, Helm, Kustomize, and service mesh technologies.
  • Proficiency in Terraform, Pulumi, Ansible, configuration management, state management, modularity, and GitOps.
  • Experience designing and operating continuous integration and continuous delivery systems using Jenkins, Spinnaker, ArgoCD, or GitHub Actions, including creating pipelines for large teams.
  • Proficiency in at least two systems programming languages: Python, Go, or Java.

Responsibilities

  • Define and own the multi-year technical roadmap for DevOps infrastructure, continuous integration and continuous delivery platforms, and cloud architecture.
  • Establish architecture standards, reference designs, and platform blueprints for team adoption.
  • Lead the adoption of infrastructure tooling, cloud services, and AI-driven automation frameworks while considering trade-offs, cost, and operational impact.
  • Implement company-wide initiatives to improve developer productivity, accelerate deployment, and enhance platform reliability, with clear goals and progress tracking.
  • Oversee major infrastructure migrations, platform consolidations, and re-architectures.
  • Collaborate with site reliability engineering, Security, and Compliance to design platforms for reliability, observability, and security.
  • Identify and resolve software development issues involving reliability, scalability, and ease of development, leading the team in solving these problems.
  • Develop and execute platform strategies for AI and machine learning workload delivery, including model training infrastructure, large language model inference serving, feature pipelines, and AIOps integration.
  • Mentor senior and staff engineers, conduct architecture reviews, provide design feedback, and improve technical standards.

Desired Qualifications

  • 15 or more years of experience in infrastructure or platform engineering, especially in fast-paced, large-scale consumer-facing technology environments.
  • Experience architecting end-to-end machine learning operations platforms, including model registries, experiment tracking, automated retraining pipelines, A/B testing infrastructure, and production model observability.
  • Expertise in large language model infrastructure, including hosting and fine-tuning large language models, retrieval-augmented generation pipelines, Model Context Protocol server creation and integration, vector databases, and prompt engineering at scale.
  • Experience implementing AIOps solutions that automate or augment on-call operations, including predictive alerting, automated root cause analysis, self-healing runbooks, and capacity forecasting.
  • Familiarity with AI safety and governance, including model drift detection, bias monitoring, explainability, and audit trails.
  • Understanding of FinOps principles applied to AI workloads, including GPU cost optimization, spot instance strategies, and inference cost modeling.
  • Experience building internal developer platforms with AI-assisted features such as natural language queries, AI-generated runbooks, and large-language-model-augmented incident postmortems.
  • Experience in platform modernization, including bare-metal-to-cloud migrations, monolith decomposition, and legacy continuous integration and continuous delivery re-platforming.
  • Experience with edge computing, content delivery network architecture, and globally distributed cache and content delivery strategies for large-scale web properties.
  • Hands-on experience in chaos engineering and advanced reliability practices, including failure injection, game days, capacity modeling, and traffic shaping.
  • A record of publishing architecture decisions, internal white papers, or cross-organizational requests for comments that influenced platform direction.
  • Contributions to open-source infrastructure or artificial intelligence and machine learning tooling projects, or active participation in DevOps and AI engineering communities.
  • Understanding of application and infrastructure security, including zero-trust security, secrets management, vulnerability management, and compliance frameworks.
  • Verbal and written communication skills for presenting complex architectural trade-offs to engineering and executive audiences.
  • Cloud, DevOps, and AI certifications.

About the Company

Apple

Designs phones, computers and their operating systems

Apple designs and sells consumer electronics, from iPhone smartphones and Mac computers to tablets and wearable devices. Its operating systems manage apps and built-in functions for communication and work, while iCloud stores files and synchronizes data across devices. Customers buy additional digital services through subscriptions such as Apple Music and Apple TV, and developers distribute software through the App Store. Apple earns money from hardware sales, subscriptions and digital transactions, selling directly to customers and through retailers and mobile carriers around the world. It was founded in 1976 and has its headquarters in Cupertino, California.

Company size
10,001+
Company stage
IPO
Headquarters
Cupertino, CA
Founded
1976

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