AI Architect

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 dedicated individual who is passionate about crafting, implementing, and operating AI solutions that have a direct and measurable impact on Apple Sales and its customers. We’re seeking a visionary US DI AI Platform Architect to lead the design and evolution of our internal AI orchestration layer—powering intelligent agents, embedding expert systems, and integrating GenAI capabilities across our sales data ecosystem. This role will work directly with product, engineering, and executive stakeholders to architect and scale a modular, future-proof AI infrastructure aligned with our AI strategy.

Description

This role will operate in both capacities, to augment existing AI roadmap, as well as innovate and trailblazing new frontier tech projects, crafting AI experiences that reduce time to insights and catalyze decision making. AI is a team sport, and in your role, you will be key in leading and influencing teams on the translation of business problems and questions into GenAI solutions. In this role, you will: - Architect the core GenAI orchestration platform, including routing logic, agent specialization, fallback handling, and metadata logging. - Design modular APIs, SDKs, and microservices to integrate LLMs, retrieval-augmented generation (RAG), traditional ML models, and data pipelines. - Drive interoperability with existing ML systems (e.g., forecasting, attribution, anomaly detection) and support downstream apps like dashboards, web tools, and chat interfaces. - Partner closely with data science, engineering, and sales ops to embed context-aware intelligence in decision-making tools. - Lead technical decision-making on infrastructure components, embedding safety mechanisms (e.g., autonomy sliders, grounding checks, model monitoring). - Build scalable pipelines for multi-modal agent input, memory, and semantic routing. - Contribute to hiring and mentoring a cross-functional team of engineers and scientists. - Collaborate closely with business teams to incorporate AI into their weekly cadences.

Minimum Qualifications

8+ years of experience in data and AI-related fields such as software architecture or AI engineering, software development, data science, data analysis, or data lead roles, with experience across both traditional ML systems and GenAI LLMs. Eagerness and ability to learn new skills and solve dynamic problems in an encouraging and expansive environment. Ability to lead the development of AI projects from start to finish. Comfort with ambiguity. Ability to architect a full orchestrator and business context layer for sales. Applied knowledge of GenAI and RAG strategies, microservices, MCP, A2E, recommendation systems, and prompt engineering. Deep knowledge of LLM ecosystems (OpenAI, Anthropic, Gemini, etc.), RAG pipelines, vector databases (e.g., Pinecone, FAISS, Milvus, PostgreSQL). Proficiency in SQL and experience with at least one major data analytics platform, such as Hadoop, Spark, or Snowflake. Experience with API management, orchestration layers (e.g., LangChain, Semantic Kernel, Haystack), and prompt engineering best practices. Proficiency in programming languages, tools, and frameworks like Python, Git, Notebooks, Dataiku, and Streamlit. Familiarity with telemetry and evaluation frameworks for AI agents. Experience working with data science teams on insights generation leveraging LLMs. Knowledge of project management, productivity, and design tools such as Wrike and Sketch. Strong time management skills with the ability to collaborate across multiple teams. Proven experience designing scalable, cloud-native platforms (e.g., AWS, GCP, or on-prem hybrid). Able to balance competing priorities, long-term projects, and ad hoc requirements. Ability to work in a fast-paced, dynamic, constantly evolving business environment. B.S Degree in Computer Science/Engineering, or equivalent work experience.

Preferred Qualifications

Strong experience articulating and translating business questions into AI solutions. Ability to communicate results and insights effectively to partners and senior leaders, as well as both technical and non-technical audiences. Experience with anomaly detection and causal inference models. Sound communication skills - adept at messaging domain and technical content, at a level appropriate for the audience. Strong ability to gain trust with customers and senior leadership. Proven experience working with LLMs and GenAI frameworks (LangChain, LlamaIndex, etc.). Familiarity with embedding, retrieval algorithms, agents, and data modeling for vector development graphs. Proficiency with 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 are preferred.