Gen AI Software Engineer

Role Number: 200626218-0836

Summary

Do you love creating elegant solutions to highly complex challenges? Do you intrinsically see the importance in every detail? As part of our Silicon Technologies group, you'll help build AI-driven solutions that solve pressing business challenges. You'll ensure Apple products and services can seamlessly and efficiently handle the tasks that make them beloved by millions. Joining this group means you'll be responsible for crafting and building the technology that fuels Apple's devices.

We are looking for an individual who is passionate about joining Apple's engineering team as a Gen AI Software Engineer to design and build generative AI systems and agentic workflows for our domains.

Description

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

Minimum Qualifications

• 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 10 years of relevant industry experience.

Preferred Qualifications

• 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