Do you want to help build some of the largest and most consequential enterprise and customer technology systems in the world? Join Apple’s Information Systems and Technology (IS&T) organization. IS&T is the engine behind everything Apple does for customers and for the people who build for them. It’s Apple’s central nervous system. Supporting 2.5 billion active Apple devices, processing billions of secure transactions, and keeping the technology that defines modern life running flawlessly, IS&T makes the impossible feel effortless.
Do you love building solutions to handle global complexity and immense scale? Imagine what you could do here.
AI & Data Platforms (AiDP) is IS&T's engine for AI-powered innovation. The team brings together data, application development, and machine learning - including generative AI - along with data services and customer success functions, to help IS&T build solutions more efficiently and streamline the adoption and embedding of generative AI across Apple.
Description
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.","responsibilities":"Translate complex business requirements into scalable technical solutions meeting data warehousing/analytics design standards.
Strong understanding of analytics needs and proactive-ness to build solutions to improve the efficiency along with that help implement leading data practices & standards.
Collaborate with multiple multi-functional teams and work on solutions which has larger impact on Apple business.
Ability to communicate effectively, both written and verbal, with technical and non- technical multi-functional teams.
You will get along with many other internal/external teams to deliver elite products in an exciting rapidly changing environment.
Ability to manage partner communication & project risks.
Thrives in a multifaceted environment, maintaining composure and a positive attitude.
Preferred Qualifications
- 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
Minimum Qualifications
- Bachelor's degree or equivalent experience, with 4+ years in data engineering and applied AI/ML engineering
- Proven experience designing and building end-to-end data pipelines (batch, near real-time, real-time) at scale, with modern data warehousing/lakehouse technologies such as Snowflake, Spark, Iceberg, SingleStore, Kafka, Cassandra, or HANA - petabyte-scale environments preferred
- Strong programming proficiency in Python (required), with familiarity in Scala/Java for big data processing, and advanced SQL skills including dimensional data modeling
- Demonstrated 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
- 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
