Sr Software Engineer (agentic Ai), AI & Data Platforms

Apple Apple · Big Tech · Austin, TX · Machine Learning and AI

This role focuses on building the backbone of an AI-powered developer platform that uses AI agents and autonomous workflows to support app development within Apple. The engineer will design and maintain backend services and orchestration systems for multi-agent AI workflows, integrate LLMs into developer tools, and work on cloud deployments and MLOps.

What you'd actually do

  1. Design and maintain backend services and orchestration systems that power multi-agent AI workflows.
  2. Build reliable systems that integrate large language models (LLMs) into developer tools and services.
  3. Work on cloud deployments, CI/CD, and MLOps practices to keep AI services reliable and scalable.
  4. Collaborate with researchers and engineers to bring new AI capabilities into production.
  5. Partner with developers to understand their needs and improve the experience of using AI-powered tools.

Skills

Required

  • 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)
  • Solid experience designing and developing distributed systems, backend services, and APIs in production environments

Nice to have

  • 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

What the JD emphasized

  • AI agents
  • autonomous workflows
  • multi-agent AI workflows
  • Large Language Models (LLMs)
  • Retrieval-Augmented Generation (RAG)

Other signals

  • AI agents
  • autonomous workflows
  • LLMs
  • backend and distributed systems expertise