Software Engineering (AI/ML), Retail and Marcom Engineering

Apple • Austin, TX

1d

About The Position

Apple’s Information Systems and Technology (IS&T) organization supports technology used across Apple’s customer and enterprise systems. Within IS&T, Retail and Marcom Engineering builds and operates systems and experiences for Apple’s online and physical stores, as well as interactive marketing experiences and tools for creative operations.

The Senior AI/ML Engineer will build and scale intelligent systems for Apple’s globally led creative marketing team. The role drives end-to-end development and deployment of AI/ML solutions, including production systems involving generative AI, large language models (LLMs), Graph RAG, and multimodal models. It includes engineering conversational and agentic workflows and developing AI/ML experiences that serve customers globally.

The engineer will manage the machine-learning lifecycle from conception through production, collaborate with business and engineering stakeholders to turn requirements into scalable solutions, and develop pipelines and applications using LLM and GenAI technologies. The role also establishes engineering best practices for LLM, RAG, and conversational AI implementations, with attention to performance, reliability, and maintainability. In addition, the engineer builds and deploys AIOps solutions for clustering, classification, anomaly detection, and capacity prediction.

Requirements

• At least 5 years of hands-on experience implementing AI/ML models in enterprise systems.
• Knowledge of supervised and unsupervised learning, deep learning, and anomaly detection.
• Expertise in NLP, NLU, and conversational AI, including intent recognition, dialogue management, and natural language generation.
• Experience taking GenAI solutions, including LLMs and RAG, into production, and a solid understanding of LLM architectures.
• Mastery of Python and AI/ML libraries and frameworks such as Pandas, NumPy, TensorFlow, and PyTorch.
• Ability to integrate AI and bot systems with backend systems, databases, and APIs to deliver end-to-end solutions.
• Troubleshooting and analytical skills, including diagnosing complex issues in distributed environments.
• BS or MS in Engineering, Computer Science, or a related field, or equivalent practical experience.

Nice To Haves

• Hands-on experience building and deploying Graph RAG pipelines, including graph databases and vector stores.
• Knowledge of DevOps practices, including CI/CD pipelines, infrastructure as code such as Terraform, Docker, and Git-based workflows for automated ML-service build, test, and deployment.
• Knowledge of SRE principles, including defining SLIs and SLOs, monitoring and alerting, incident response, root cause analysis, and production reliability.
• Familiarity with RAG benchmarking and evaluation methods, such as faithfulness, context recall, LLM-as-judge, and golden datasets.
• Experience fine-tuning LLMs using methods such as LoRA or PEFT, and selecting fine-tuning over prompting or RAG when appropriate.
• Experience building solutions with multimodal models and agentic workflows, including tool use, orchestration, and failure handling.
• Experience designing, building, and deploying AI/ML platforms and services at scale, handling high data volumes and traffic.
• LLMOps experience, including production evaluation, guardrails, observability, and cost and latency optimization.
• Experience building AIOps solutions such as clustering, classification, anomaly detection, and capacity prediction.
• Familiarity with cloud-native and GPU-backed inference environments, such as Kubernetes and model serving.
• Track record of mentoring engineers, defining reference architectures, and setting best practices for LLM, RAG, and conversational AI.
• Experience applying GenAI to content, creative, or marketing workflows.
• Communication and collaboration skills, including explaining technical concepts to technical and non-technical audiences.

Responsibilities

• Design, build, and deploy intelligent AI/ML experiences and production systems.
• Drive the end-to-end machine-learning lifecycle from conception to production.
• Develop scalable pipelines and applications leveraging LLM and generative AI technologies.
• Build conversational and agentic workflows using technologies such as LLMs, Graph RAG, and multimodal models.
• Partner with business and engineering stakeholders to translate complex requirements into scalable AI/ML solutions.
• Establish engineering best practices for LLM, RAG, and conversational AI implementations.
• Ensure AI/ML services have high performance, reliability, and maintainability.
• Build and deploy AIOps models and solutions for clustering, classification, anomaly detection, and capacity prediction.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior