Senior AI Engineer

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 theres no telling what you could 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 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.

Were 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. Youll be responsible for building testing and optimizing intelligent agents retrieval pipelines and embedded AI features across our sales data platforms.

Design prototype and productionize LLM-powered applications that combine structured data unstructured knowledge semantic layers and internal business logicnBuild agentic AI systems that can retrieve context reason across data sources call tools and APIs generate insights and support business with product data science design engineering and business stakeholders to translate ambiguous business problems into practical AI solutionsnBuild modular APIs SDKs and micro-services to integrate LLMs RAG pipelines traditional ML models data pipelines and enterprise secure and reliable integrations between LLMs internal APIs databases knowledge sources and enterprise closely with data science engineering and sales ops to embed context-aware intelligence in decision-making technical decision-making on infrastructure components embedding safety mechanisms (e.g. autonomy sliders grounding checks model monitoring).nBuild scalable pipelines for multi-modal agent input memory and semantic closely with business teams to incorporate AI into their weekly fast experimentation with production readiness ensuring AI capabilities are scalable measurable reliable and maintainable.

10 years of experience in ML software engineering or data science with recent focus on Applied AI and to lead development of AI projects from start to in Python (FastAPI LangChain or similar frameworks) context engineering and RESTful API -on experience with LLM APIs embeddings vector databases and RAG grounding in data structures async programming and pipeline with agent orchestration frameworks such as LangGraph Google ADK CrewAI AutoGen or similar with Claude Code-style agentic engineering patterns including subagents hooks MCP integrations permissions and session-based for action curiosity and a collaborative with telemetry and evaluation frameworks for AI 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 to balance competing priorities long-term projects and ad hoc to work in a fast-paced dynamic constantly evolving business with rapid prototyping reproduction and validation of research looking for someone with an eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive .S Degree in Computer Science/Engineering or equivalent work experience

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 to balance rapid prototyping with production readiness especially when moving from proof-of-concept to scalable enterprise experience articulating and translating business questions into AI results and insights effectively to partners and senior leaders as well as both technical and non-technical 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 with embeddings retrieval algorithms knowledge graphs vector databases hybrid retrieval reranking and graph-based approaches to enterprise knowledge complementary technologies for distributed systems architecture and asynchronous messaging agent communication and catching like RabbitMQ Redis and Valkey are Degree (MS or Ph.D.) in Economics Electrical Engineering Statistics Data Science or a similar quantitative field is preferred.