The Enterprise Data warehouse team within AiDP deals with Petabytes of data catering to a wide variety of real- time, near real-time and batch pipelines and data centric agentic solutions on these data assets. These solutions are integral part of business functions like Retail, Sales, Operations, Finance, AppleCare, Marketing and Internet Services, enabling business drivers to make critical decisions. You should be able to (i) understand a business challenge, (ii) Collaborate with business and other cross functional teams (ii) design a statistical or deep learning solution to find the needed answer to it, (iii) developing it by yourself or guide another person to do it, (iv) deliver the outcome into production, (v) Keep a good governance of your work. There are massive opportunities for you deliver impactful influences to Apple.

Minimum Qualifications

Bachelor's degree or equivalent experience, with 2+ years in software and applied AI/ML engineering
Programming proficiency in Python or Java
Experience building GenAI-powered applications, including RAG pipelines, embeddings, vector databases, and semantic search integrated with structured/unstructured enterprise data
Practical experience building AI agents or agentic workflows (planning, memory, tool use, execution loops) using frameworks such as LangChain, LlamaIndex, or similar.

Preferred Qualifications

Full-stack application development experience - able to design and ship end-to-end data/AI products spanning backend APIs, data services, and front-end/UI layers
Ability to translate ambiguous business problems into scalable technical designs, and take solutions from prototype to production with attention to governance, reliability, and observability
Strong collaboration and communication skills to work across cross-functional business and engineering teams, technical and non-technical alike
Experience building multi-agent systems/agentic workflows that autonomously coordinate retrieval, reasoning, and action steps across a data pipeline
Experience with cloud data platforms and cloud-native architectures (AWS, GCP, or Azure — AWS preferred), along with ML frameworks such as PyTorch or TensorFlow for model training and serving
Experience with modern front-end/full-stack frameworks (e.g., React, Streamlit, FastAPI) for rapidly building internal data/AI-powered applications and dashboards
Track record of brainstorming and shipping POCs using AI/ML/GenAI services to solve new or existing enterprise problems, with the ability to manage partner communications and ambiguity in a fast-changing, matrixed environment
Prior experience in enterprise domains such as Retail, Finance, Sales, Marketing, or Operations analytics is a plus