Senior AI Engineer

** Weekly

Hours:

** 40

** Role Number:*
* ** Summary*
* Imagine what you could do here. At Apple, new ideas have a way of becoming outstanding products, services, and customer experiences very quickly. Bring passion and dedication to your job, and there's no telling what you could accomplish.

Apple's Sales organization generates the revenue needed to fuel our ongoing development of products and services. This, in turn, enriches the lives of hundreds of millions of people around the world. We are, in many ways, the face of Apple to our largest customers.

Apple's US Decision Intelligence (DI) team is looking for a talented individual who is passionate about crafting, implementing, and operating AI solutions that have a direct and measurable impact on Apple Sales and its customers.

** Description*
* We're looking for a Senior AI Engineer with strong software development skills and a passion for applying LLMs and Agentic workflows to real-world business problems. You'll be responsible for building, testing, and optimizing intelligent agents, retrieval pipelines, and embedded AI features across our sales data platforms.

** Minimum Qualifications*
* + 10+ years of experience in ML, software engineering, or data science, with recent focus on Applied AI and LLMs.

+ Ability to lead development of AI projects from start to finish.

+ Proficiency in Python (FastAPI, Lang Chain, or similar frameworks), context engineering, and RESTful API design.

+ Hands-on experience with LLM APIs, embeddings, vector databases, and RAG workflows.

+ Solid grounding in data structures, async programming, and pipeline orchestration.

+

Experience with agent orchestration frameworks such as Lang Graph, Google ADK, CrewAI, Auto Gen, or similar frameworks.

+ Familiarity with Claude Code-style agentic engineering patterns, including subagents, hooks, MCP integrations, permissions, and session-based workflows.

+ Bias for action, curiosity, and a collaborative mindset.

+ Familiarity with telemetry and evaluation frameworks for AI agents.

+ Ability to design business-context layers that combine structured data, semantic definitions, user permissions, domain logic, and unstructured knowledge to produce grounded AI responses. Strong time management skills with the ability to collaborate across multiple teams.

+ Able to balance competing priorities, long-term projects, and ad hoc requirements.

+ Ability to work in a fast-paced, dynamic, constantly evolving business environment.

+ Comfortable with rapid prototyping, reproduction, and validation of research ideas.

+ We're looking for someone with an eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive environment.

+ B.S Degree in Computer Science/Engineering, or equivalent work experience

** Preferred Qualifications*
* + Hands-on experience building production-grade AI agents, including tool calling, routing, multi-step reasoning flows, agent handoffs, memory/session management, and human-in-the-loop patterns.

+ Ability to balance rapid prototyping with production readiness, especially when moving from proof-of-concept to scalable enterprise features.

+ Strong experience articulating and translating business questions into AI solutions.

+ Communicate results and insights effectively to partners and senior leaders, as well as both technical and non-technical audiences.

+ Sound communication skills - adept at messaging domain and technical content, at a level appropriate for the audience. Strong ability to gain trust with stakeholders and senior leadership.

+ Familiarity with embeddings, retrieval algorithms, knowledge graphs, vector databases, hybrid retrieval, reranking, and graph-based approaches to enterprise knowledge modeling.

+ Other complementary technologies for distributed systems architecture and asynchronous messaging, agent communication, and catching like RabbitMQ, Redis, and Valkey are preferred.

+ Advanced Degree (MS or Ph.D.) in Economics, Electrical Engineering, Statistics, Data Science, or a similar quantitative field is preferred.