Job description

We're seeking a talented Senior AI/ML Engineer to build and scale the intelligent systems behind Apple's globally led creative marketing team. You will drive the end-to-end development and deployment of cutting-edge AI/ML solutions, directly influencing how millions of Apple customers experience our brand. You'll design, build, and optimize production systems involving GenAI, LLMs, Graph RAG, and multi-modal models, engineering intelligent conversational and agentic workflows that redefine what's possible. If you're ready to build and ship high-impact AI/ML solutions at a global scale, this is your opportunity. As a Senior AI/ML Engineer, you'll design, build, and deploy intelligent experiences, driving the end-to-end machine learning lifecycle from conception to production. Partnering with business and engineering stakeholders, you'll translate complex requirements into scalable AI/ML solutions, developing robust pipelines and applications that leverage LLM and GenAI technologies to unlock new insights at scale. Your hands-on expertise will help establish engineering best practices for LLM, RAG, and conversational AI implementations, ensuring high performance, reliability, and maintainability across our services. You'll also build and deploy advanced AIOps solutions, implementing models for clustering, classification, anomaly detection, and capacity prediction to optimize our systems. 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 (LLMs and RAG) into production, with a solid understanding of LLM architectures. Mastery of Python and key AI/ML libraries and frameworks (Pandas, NumPy, TensorFlow, 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. 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 (e.g., Terraform), containerization (Docker), and Git-based workflows for automated build, test, and deployment of ML services. Knowledge of SRE principles, including defining SLIs/SLOs, monitoring and alerting, incident response, root cause analysis, and building for reliability in production systems. Familiarity with RAG benchmarking and evaluation methodologies (e.g., faithfulness, context recall, LLM-as-judge, golden datasets). Experience fine-tuning LLMs (e.g., LoRA, PEFT) and knowing when to choose fine-tuning over prompting or RAG. Experience building solutions with multi-modal models and agentic workflows (tool use, orchestration, 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 (e.g., Kubernetes, 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, with the ability to explain technical concepts to technical and non-technical audiences.

Requirements

Must-haves

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 (LLMs and RAG) into production, with a solid understanding of LLM architectures.
Mastery of Python and key AI/ML libraries and frameworks (Pandas, NumPy, TensorFlow, 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.
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 (e.g., Terraform), containerization (Docker), and Git-based workflows for automated build, test, and deployment of ML services.
Knowledge of SRE principles, including defining SLIs/SLOs, monitoring and alerting, incident response, root cause analysis, and building for reliability in production systems.
Familiarity with RAG benchmarking and evaluation methodologies (e.g., faithfulness, context recall, LLM-as-judge, golden datasets).
Experience fine-tuning LLMs (e.g., LoRA, PEFT) and knowing when to choose fine-tuning over prompting or RAG.
Experience building solutions with multi-modal models and agentic workflows (tool use, orchestration, 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 (e.g., Kubernetes, 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, with the ability to explain technical concepts to technical and non-technical audiences.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningllmpythonci/cddockerkubernetes

Platform: apple
Effort: medium