In this highly visible role your primary responsibilities will include:- Designing and implementing generative AI systems including multi-agent workflows tool calling architectures LLM orchestration patterns and reusable AI components- Building agentic AI solutions that coordinate multiple AI services and tools to solve complex domain-specific problems- Collaborating with our internal multi-functional teams as well as the AIML organization at Apple to understand domain-specific needs and architect appropriate AI solutions- Enabling the organization to leverage AI capabilities and drive efficiency in chip delivery
- Python programming experience
- Experience building generative AI applications using LLMs and vector databases
- Knowledge of current Gen AI research and techniques in one or more of the following areas: RAG systems Agentic AI (multi-agent orchestration tool calling) or Prompt Engineering
- Minimum requirement of BS and 3 years of relevant industry experience
- Hands-on experience with agentic AI frameworks (e.g. LangGraph AutoGen CrewAI) for building multi-step reasoning and tool-using agents
- Familiarity with integrating Model Context Protocol (MCP) into AI workflows
- Experience building evaluation frameworks for AI systems (retrieval quality agent performance LLM outputs)
- Experience in designing and implementing information retrieval systems using embeddings vector stores (e.g. Milvus Qdrant) or similarity match & ranking techniques
- Designed and optimized RESTful services
- Comfortable working within Linux/Unix environments
In this highly visible role your primary responsibilities will include:- Designing and implementing generative AI systems including multi-agent workflows tool calling architectures LLM orchestration patterns and reusable AI components- Building agentic AI solutions that coordinate multiple AI serv...
In this highly visible role your primary responsibilities will include:- Designing and implementing generative AI systems including multi-agent workflows tool calling architectures LLM orchestration patterns and reusable AI components- Building agentic AI solutions that coordinate multiple AI services and tools to solve complex domain-specific problems- Collaborating with our internal multi-functional teams as well as the AIML organization at Apple to understand domain-specific needs and architect appropriate AI solutions- Enabling the organization to leverage AI capabilities and drive efficiency in chip delivery
- Python programming experience
- Experience building generative AI applications using LLMs and vector databases
- Knowledge of current Gen AI research and techniques in one or more of the following areas: RAG systems Agentic AI (multi-agent orchestration tool calling) or Prompt Engineering
- Minimum requirement of BS and 3 years of relevant industry experience
- Hands-on experience with agentic AI frameworks (e.g. LangGraph AutoGen CrewAI) for building multi-step reasoning and tool-using agents
- Familiarity with integrating Model Context Protocol (MCP) into AI workflows
- Experience building evaluation frameworks for AI systems (retrieval quality agent performance LLM outputs)
- Experience in designing and implementing information retrieval systems using embeddings vector stores (e.g. Milvus Qdrant) or similarity match & ranking techniques
- Designed and optimized RESTful services
- Comfortable working within Linux/Unix environments
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