The MLOps Engineer will play a crucial role in maintaining and enhancing the machine learning infrastructure, ensuring that AI/ML systems are reliable and scalable. This position bridges the gap between data science and engineering, focusing on operational excellence throughout the ML lifecycle. The engineer will be responsible for building, deploying, and optimizing AI/ML applications while establishing best practices for model integration and monitoring. Collaboration with cross-functional teams will be essential to drive quality initiatives across various stages of the ML process.

• 8 years of experience in software engineering with a focus on large-scale software system design and implementation.
• Bachelor's Degree in Software Engineering, Computer Science, Statistics, Data Mining, Machine Learning, Operations Research, or a related field.
• Proven track record of shipping and maintaining production-grade ML systems end-to-end.
• Strong experience with distributed systems, databases (SQL/NoSQL), and cloud platforms (AWS, Azure, or GCP).
• Hands-on experience with MLOps tooling and platforms such as Ray, MLflow, Kubeflow, SageMaker, or Vertex AI.
• Proficiency in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Experience building and managing CI/CD pipelines for ML workflows using tools such as Jenkins, GitHub Actions, or ArgoCD.
• Strong understanding of data pipeline orchestration tools such as Airflow or Prefect.
• 10 years of related experience building high-throughput, scalable applications or machine learning models in a production environment.
• Familiarity with model monitoring, drift detection, and observability practices in production environments.
• Excellent cross-functional communication skills.

• Drive end-to-end quality initiatives across data ingestion, model training, deployment pipelines, and MLOps tooling.
• Build, deploy, and optimize AI/ML based applications with an emphasis on scalable, production-ready systems.
• Establish standard methodologies for model integration, deployment, and monitoring using CI/CD principles.
• Collaborate effectively across engineering and data science teams to ensure operational excellence.
• Evaluate and validate LLM-generated outputs for accuracy and reliability before applying them in production contexts.
• Incorporate AI-assisted tools into day-to-day engineering workflows, understanding their limitations and appropriate use cases.

• Employees at Apple are often offered comprehensive benefits that support physical and mental well-being—flexible medical plans, confidential counseling, onsite wellness centers at major campuses, and resources for fitness and daily life. Families typically receive fertility support, paid parental leave with gradual return, caregiving leave, and dependent-care guidance, while financial perks commonly include stock grants (with purchase discounts), 401(k) matching, and income-protection coverage. Employees also see robust time off, Apple University learning and tuition reimbursement, donation matching and paid volunteer hours, and deep product and partner discounts.