DevOps/Backend Software Engineer

As a DevOps/Backend Engineer in our team, you will design, implement, and manage the infrastructure that powers all of our services, tools, and apps. You will work with a diverse array of cross-functional partners throughout Apple on challenging projects incorporating Machine Learning, Generative AI, and MLOps — collaborating closely with ML engineering teams to integrate, evaluate, and operationalize models within our services. The services and software you craft will be instrumental in solving difficult challenges, providing data insights, and driving decision-making within Hardware Engineering and beyond. We move at a fast pace, iterate quickly, and work side-by-side with our customers to ensure we’re building the most effective solutions possible. Minimum Qualifications 3+ years experience in SRE/DevOps, systems engineering, build/release/deployment, and/or automation Proficient in implementing applications in private/public cloud infrastructure and container technologies, including but not limited to: Kubernetes, Docker, database platforms, and event/data pipelines, and model-serving or API integration patterns Experience designing, building and managing CI/CD pipelines Experience with networking load balancers such as HAProxy, NGINX, etc. Demonstrated ability to write applications in a high-level programming language like Python, Ruby, Java, etc. Excellent written and verbal communication skills to both technical and non-technical audiences Bachelor’s Degree in Computer Science, Computer Engineering, related field, or equivalent work experience Preferred Qualifications Experience building scalable, maintainable, robust web-services and applications Ability to architect complex systems in a reusable, modular way Experience building infrastructure to support Generative AI services, including model integration, evaluation pipelines, and A/B testing frameworks Familiarity with MLOps practices such as model versioning, pipeline orchestration, experiment tracking, and performance monitoring in production environments Curiosity to learn new technologies and passion for sharing that knowledge with others Master’s degree in Computer Science, Computer Engineering, related field, or equivalent work experience