Agentic AI Developer - Agent Development

Summary

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something. There are countless ways you can contribute here, from working on apple.com to software for digital & physical Apple Stores, from software for Apple Care to Apple-wide systems for identity, operations, recruiting, and people management, from software for Apple data centers to Apple-wide data processing & AI/ML systems. Apple's IS&T team builds the tools, platforms, and services that power the people who build Apple. Our Customer Systems group is reimagining how Apple engineers work through intelligent, autonomous systems — and we're looking for an Agentic AI Developer to help lead that effort.

Minimum Qualifications

4+ years of software engineering experience, with at least 2 years focused on AI/ML systems, LLM integrations, or autonomous agent development Hands-on experience designing and implementing multi-agent orchestration systems and agent loop patterns (ReAct, Plan-and-Execute, or similar) Strong proficiency in Java (Spring Boot) and TypeScript/React for building full-stack agentic applications Solid understanding of LLM fundamentals: prompt engineering, context window management, function calling, structured outputs, and tool use Experience building RAG pipelines and working with vector databases . Demonstrated ability to implement agent safety guardrails including prompt-injection defences and output schema enforcement Experience with observability tooling (OpenTelemetry or equivalent) applied to distributed, AI-driven systems Bachelor's degree in Computer Science, Software Engineering, or a related technical field .

Preferred Qualifications

Experience implementing or consuming MCP (Model Context Protocol) servers and clients Familiarity with model routing and fallback strategies across multiple LLM providers Reactive programming in Java — Project Reactor, WebFlux — for streaming LLM responses Python proficiency for agent prototyping and evaluation pipeline scripting Hands-on experience with cloud AI platforms . Experience with agent sandboxing and safe code execution environments Awareness of fine-tuning, LoRA, and small-model distillation techniques for agent specialisation

Description

We are looking for a talented Agentic AI Developer to join Apple's IS&T Customer Systems team. In this role, you will design, build, and operate intelligent autonomous agent systems end-to-end — both client-side and server-side. You will architect agents that reason, plan, remember, and act reliably across long-running workflows, with deep attention to memory design, multi-agent orchestration, observability, and maintainability. This is a deeply technical engineering role with a high bar for production quality. You will own the full lifecycle of agentic systems: from agent architecture and memory design, to tool integration, observability, and safe deployment. While the primary focus is agent engineering, a solid understanding of the Java tech stack, React, and TypeScript is also important, as you will work across both server-side and client-side surfaces. You will collaborate closely with engineers, product managers, and platform teams across Apple to bring AI capabilities into the developer tools our teams depend on every day.

Responsibilities

Design and implement multi-agent orchestration systems — including agent collaboration, task delegation, hand-offs, shared context management, role specialisation, and conflict resolution patterns Build agent memory architectures — episodic, semantic, and working memory — using vector stores (pgvector, Milvus) and hybrid retrieval strategies to enable agents to reason over past interactions and maintain state across sessions Develop tool and function-calling layers that expose internal APIs and services as agent-executable tools via MCP (Model Context Protocol) servers or custom tool registries, with strong contracts and well-defined failure semantics Implement RAG (Retrieval-Augmented Generation) pipelines with vector stores and hybrid retrieval strategies to ground agent responses in authoritative internal knowledge Own the observability story for agentic systems — distributed tracing of agent runs, prompt and response capture, token and cost accounting, evaluation pipelines, replay and debugging, and regression detection using OpenTelemetry and LLM observability tooling Drive maintainability of agentic systems through versioning of prompts, tools, and agent graphs; deterministic test harnesses; evaluation suites; safe canary rollouts; and structured logging that makes non-deterministic behaviour debuggable Apply agent safety guardrails — input validation, prompt-injection defences, output schema contracts, and sandboxed code execution — to ensure systems are safe and reliable at production scale Architect and maintain server-side agent services using Java (Spring Boot, Spring AI) and client-side agent interfaces using React and TypeScript