AIOps Software Engineer - Strategic Data Solutions

Apple•Austin, TX

About The Position

Join Apple’s SWE AIOps team supporting Strategic Data Solutions (SDS) operations across real-time decision systems, large-scale platforms, and thousands of services and jobs operating on petabytes of data. The team develops customer data solutions and tools that support Apple engineering and operations teams, using modern technologies and collaboration across Apple. As a member of a multifunctional team, you will address software engineering and operational challenges across SDS services. You will support analytics and real-time platforms, develop applications for analytical and operational needs, and work with external teams on infrastructure requirements. You will help design, architect, build, and maintain applications and systems using cloud and AI technologies, while driving operational excellence and improving automation and tooling. The role also includes providing hardware architecture guidance, estimating cluster capacity, creating roadmaps, and providing technical leadership to strengthen operational maturity and skills across the team. You will work closely with machine learning engineers, other SDS teams, and Apple business partners. The role calls for independent work, effective prioritization and time management, strong collaboration, and communication adapted to diverse stakeholders. Apple also emphasizes leveraging different experiences and perspectives.

Requirements

BS or MS in Computer Science preferred; equivalent work experience is considered.
8+ years of work experience in Java, Scala, Python, or an equivalent programming language.
8+ years of work experience in software engineering, data engineering, or systems engineering involving the full development lifecycle.
Fundamental understanding of computer science, including data structures and algorithms.
Hands-on design and development experience with Big Data and NoSQL technologies such as Iceberg, S3, Cassandra, or Elasticsearch.
Proven expertise building and accelerating software development workflows using AI coding assistants and LLMs such as Claude.
Experience deploying applications using Docker and Kubernetes.
Understanding of the design and development of large-scale, high-throughput, low-latency applications.
Experience addressing architectural and design considerations such as performance, scalability, reusability, and flexibility.
Experience establishing standards, operational excellence, and delivery practices that other SDS teams can adopt.
Ability to diagnose and resolve complex technical and operational issues escalated by support and development teams.
Ability to solve problems in collaboration with business partners.
Proven technical leadership and communication skills.
Strong aptitude and ability to learn new technologies quickly.

Nice To Haves

Experience developing batch and streaming Spark applications.
Understanding of best practices and deploying pipelines in cloud technologies such as AWS.
Experience leveraging AI-driven application performance monitoring tools for predictive failure detection and automated root-cause analysis.
Experience with large-scale real-time decisioning.
Ability to apply others’ diverse experiences, styles, backgrounds, and perspectives to achieve results.
Ability to contribute to a work climate where differences are valued and supported.

Responsibilities

Support analytics and real-time platforms across SDS services.
Develop applications that address complex analytical and real-time operational questions.
Work with external teams to drive infrastructure development needs.
Help design, architect, build, and maintain applications and systems using cloud and AI technologies.
Drive operational excellence across business-critical platforms through exploration, benchmarking, and implementation of resilient operations and systems engineering solutions.
Identify opportunities to improve automation and tools.
Provide hardware architectural guidance, estimate cluster capacity, and create a roadmap.
Provide technical leadership focused on increasing operational maturity and skills across the team.
Work closely with machine learning engineers, other SDS teams, and Apple business partners.
Adapt communication content and style to meet the needs of diverse stakeholders.
Help ensure different experiences, styles, backgrounds, and perspectives are leveraged appropriately.