Full-Stack AI Engineer

Summary

At Apple, new ideas have a way of becoming great products, services, and customer experiences very quickly. The Greater China Channel Sales DMP team drives digital transformation through platforms that help our channel sales organization plan, execute, measure, and settle business initiatives. Our solutions directly inform critical business decisions and influence financial outcomes. Do you enjoy solving complex problems, challenging assumptions, and turning ambiguity into practical solutions? We are looking for a backend or full-stack engineer with strong engineering fundamentals, attention to detail, and the curiosity to learn quickly. You will work directly with business stakeholders and own projects throughout their lifecycle, from clarifying the problem and shaping the solution to implementation, deployment, and ongoing operation. This role offers the autonomy to make architectural decisions and the responsibility to deliver reliable results. AI is part of both what we build and how we build it. You will integrate AI capabilities where they bring meaningful business value and use coding agents as a regular part of software delivery. We value hands-on experience, thoughtful task decomposition, effective context management, and rigorous review and verification, with clear ownership of everything that reaches production.

Minimum Qualifications

3+ years of professional experience building and operating production web backends or full-stack applications. Proficiency in at least one backend language and its ecosystem, such as Python, Java with Spring Boot, or JavaScript/TypeScript with Node.js. Strong software engineering fundamentals, including system design, automated testing, performance tuning, debugging, deployment, and production maintenance. Strong data modeling and relational database skills, including MySQL or PostgreSQL, SQL optimization, and caching strategies that account for consistency and invalidation. Hands-on application of DDD in real projects, with the ability to explain domain boundaries and the trade-offs behind modeling decisions. Practical experience integrating LLMs into applications or workflows, including model APIs, prompt design, and evaluation of output quality and failure cases. Regular, hands-on use of coding agents on production code, with demonstrated ability to scope tasks, provide context, critically review output, and verify correctness before release. Ability to communicate directly with business stakeholders, navigate ambiguity, and independently drive projects from problem definition through delivery.

Preferred Qualifications

Front-end development experience with TypeScript and React or Vue, enabling end-to-end ownership of user-facing features. Experience with retrieval-augmented generation (RAG), vector databases such as Milvus or Pinecone, or AI orchestration frameworks such as LangChain or LlamaIndex, with an understanding of when these approaches are appropriate. Experience with non-relational data stores such as MongoDB or Redis, message queues, and asynchronous processing. Experience building and operating cloud infrastructure on AWS or Alibaba Cloud, using Docker, Kubernetes, and continuous integration and delivery (CI/CD) pipelines. Strong understanding of web security, including authentication, authorization, secure data handling, encryption, and risks introduced by model and tool integrations. Experience building custom coding agents or internal developer tools, such as reusable skills, Model Context Protocol (MCP) servers, hooks, or automated quality gates. Experience helping teams adopt agent-assisted engineering practices, with evidence of their impact on delivery speed, quality, and maintainability.

Description

* Partner directly with business stakeholders to identify opportunities, clarify ambiguous requirements, challenge assumptions constructively, and translate business needs into scalable technical solutions.. * Apply Domain-Driven Design (DDD) to define bounded contexts, entities, value objects, and aggregates, keeping system boundaries and business logic aligned with evolving requirements. * Design and build maintainable backend services, RESTful or GraphQL APIs, and reusable business logic. Collaborate across disciplines to deliver integrated, responsive web experiences. * Develop AI-powered capabilities and agent workflows that connect large language models (LLMs), business services, and user interfaces, with appropriate data access controls, evaluation, and failure handling. * Define technical architecture from the ground up, including data models, service boundaries, caching, and orchestration. Incorporate retrieval, vector search, and model orchestration where the use case warrants them. * Build comprehensive unit, integration, and performance tests. Apply test-driven development (TDD) and behavior-driven development (BDD) to protect business behavior and support reliable agent-assisted delivery. * Own production performance, stability, security, and availability. Diagnose database bottlenecks, cache invalidation issues, and concurrency failures, and manage latency, reliability, and cost in AI-enabled workflows. * Use coding agents throughout delivery: decompose work into executable specifications, provide relevant repository context and constraints, review generated changes, and verify results through automated tests and end-to-end validation. * Evaluate emerging frameworks, AI integration patterns, and infrastructure through focused prototypes, balancing business value, operational complexity, and long-term maintainability before adoption