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

Apple • Austin, TX

14h

About The Position

Apple’s Information Systems and Technology (IS&T) organization supports Apple’s global technology systems and services. 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 that support creative operations.

The Senior AI/ML Engineer will build and scale intelligent systems supporting Apple’s globally led creative marketing team. The role involves end-to-end development and deployment of AI/ML solutions, including production systems using GenAI, LLMs, Graph RAG, and multimodal models, along with conversational and agentic workflows. The engineer will design, build, and deploy intelligent experiences across the machine-learning lifecycle, partnering with business and engineering stakeholders to translate requirements into scalable solutions and robust pipelines and applications.

The role also establishes engineering best practices for LLM, RAG, and conversational AI implementations, with a focus on performance, reliability, and maintainability. It includes developing AIOps solutions using models 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 core AI/ML concepts, including supervised and unsupervised learning, deep learning, and anomaly detection.
• Expertise in NLP, NLU, and conversational AI, including designing and deploying bots with 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.
• 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.
• Hands-on experience building and deploying Graph RAG pipelines, including graph databases and vector stores.
• Knowledge of DevOps practices, including CI/CD, infrastructure as code such as Terraform, Docker, and Git-based workflows for automated ML-service build, test, and deployment.
• Knowledge of SRE principles, including SLIs/SLOs, monitoring and alerting, incident response, root cause analysis, and production reliability.
• Familiarity with RAG benchmarking and evaluation methodologies, such as faithfulness, context recall, LLM-as-judge, and golden datasets.
• Experience fine-tuning LLMs, such as with LoRA or PEFT, and determining when fine-tuning is preferable to prompting or RAG.
• 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.
• Communication and collaboration skills, including explaining technical concepts to technical and non-technical audiences.

Nice To Haves

• Experience applying GenAI to content, creative, or marketing workflows.

Responsibilities

• Drive the complete machine-learning project lifecycle and ensure sound engineering practices and software delivery.
• Design and develop virtual assistants using conversational AI platforms.
• Collaborate with business stakeholders to identify AI/ML opportunities and develop analytic solutions using LLM technologies.
• Design, build, and deploy intelligent experiences and scalable AI/ML applications.
• Develop robust AI/ML pipelines and applications that translate complex business and engineering requirements into solutions.
• Establish engineering best practices for LLM, RAG, and conversational AI implementations.
• Build and deploy AIOps solutions for clustering, classification, anomaly detection, and capacity prediction.
• Mentor engineers, define reference architectures, and set best practices for LLM, RAG, and conversational AI.

What This Job Offers

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