7 hours ago
Responsibilities
- Design, build, deploy, and optimize production AI/ML systems using GenAI, LLMs, Graph RAG, multimodal models, and agentic workflows.
- Drive the end-to-end machine learning lifecycle from solution conception through production deployment.
- Develop conversational AI systems with intent recognition, dialogue management, natural language generation, and bot integrations.
- Build AIOps solutions for clustering, classification, anomaly detection, and capacity prediction.
- Integrate AI and bot systems with backend systems, databases, and APIs to deliver end-to-end solutions.
- Establish engineering best practices for LLM, RAG, conversational AI, evaluation, reliability, maintainability, and production operations.
- Partner with business and engineering stakeholders to translate complex requirements into scalable AI/ML solutions.
- Mentor engineers, define reference architectures, and set technical best practices.
Requirements
- At least 5 years of hands-on experience implementing AI/ML models in enterprise systems.
- Knowledge of supervised and unsupervised learning, deep learning, anomaly detection, NLP, NLU, and conversational AI.
- Experience designing and deploying bots with intent recognition, dialogue management, and natural language generation.
- Experience taking GenAI, LLM, and RAG solutions into production, including understanding of LLM architectures.
- Mastery of Python and AI/ML libraries and frameworks including Pandas, NumPy, TensorFlow, and PyTorch.
- Ability to integrate AI and bot systems with backend systems, databases, and APIs.
- BS or MS in Engineering, Computer Science, or a related field, or equivalent practical experience.
- Preferred experience with Graph RAG pipelines, graph databases, vector stores, LLM fine-tuning, multimodal models, agentic workflows, and AI/ML platforms at scale.
- Preferred knowledge of DevOps, CI/CD, infrastructure as code, containerization, Git workflows, SRE, LLMOps, evaluation, observability, and cloud-native GPU-backed inference.
- Preferred experience with Kubernetes, model serving, AIOps, content or marketing workflows, and mentoring engineers.
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About Apple
Apple designs and sells consumer electronics, software, and services for consumers and professionals worldwide, including iPhone, Mac, iPad, Apple Watch, and AirPods, plus platforms like iOS/macOS and services such as the App Store, iCloud, Music, and TV+. Its business combines device sales with services and subscriptions and in-house silicon design. Founded in 1976, Apple is headquartered in Cupertino, California, and trades on NASDAQ as AAPL.