As an MLOps Engineer, you will play a crucial role in maintaining and enhancing our machine learning infrastructure. You will ensure that AI/ML systems are reliable and scalable while driving operational excellence throughout the ML lifecycle. This position requires bridging the gap between data science and engineering, focusing on building production-ready systems. You will also establish best practices for model integration and deployment using CI/CD principles.

• 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 with the ability to collaborate effectively across engineering and data science teams.
• Comfort using LLM-based tools such as Claude, Gemini, or ChatGPT for code generation and workflow automation.
• Demonstrated ability to critically evaluate and validate LLM-generated outputs.

• 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 a strong emphasis on scalability.
• Establish standard methodologies for model integration, deployment, and monitoring.
• Collaborate with engineering and data science teams to ensure operational excellence.
• Incorporate AI-assisted tools into day-to-day engineering workflows.

• 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.