Sr Software Engineer (Agentic AI), AI & Data Platforms

The Developer Experience Platform team builds the frameworks, services and AI tools that help Apple’s enterprise developers move faster through the software development lifecycle. We’re creating a new AI-powered developer platform that brings together ai agents and autonomous workflows to support how apps are built across Apple. This is a hands-on engineering role with broad impact. You’ll contribute to building the backbone of our agentic AI platform, balancing reliability and scale with experimentation and innovation. The right candidate has strong backend and distributed systems expertise, practical experience with AI agents, and a focus on building reliable systems. Design and maintain backend services and orchestration systems that power multi-agent AI workflows. Build reliable systems that integrate large language models (LLMs) into developer tools and services. Work on cloud deployments, CI/CD, and MLOps practices to keep AI services reliable and scalable. Collaborate with researchers and engineers to bring new AI capabilities into production. Partner with developers to understand their needs and improve the experience of using AI-powered tools. Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field, or equivalent practical experience 4+ years of industry experience in software engineering or machine learning Hands-on experience building AI agents with Large Language Models (LLMs), including Retrieval-Augmented Generation (RAG), using frameworks such as LangChain, LangGraph, Pydantic AI, or CrewAI Strong programming skills in multiple languages (e.g., Python, Java, Go, Node.js, or TypeScript), with solid experience designing and developing distributed systems, backend services, and APIs in production environments Familiarity with deploying and operating services in cloud environments (AWS, GCP, or Azure), including containerization (Docker) and orchestration (Kubernetes) Experience with CI/CD pipelines and MLOps practices for deploying, scaling, and monitoring LLM-powered services Background in building REST or GraphQL APIs, microservices, and event-driven systems Knowledge of vector databases, memory systems, and human-in-the-loop workflows Strong collaboration skills with the ability to work effectively across ML research, platform engineering, and product teams

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