Cloud Data Engineer - AI/ML

Apple

About The Position

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. 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. 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.

Requirements

Bachelor's degree or equivalent experience
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)
Familiarity in Scala/Java for big data processing
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
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)
Experience 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

Nice To Haves

Prior experience in enterprise domains such as Retail, Finance, Sales, Marketing, or Operations analytics is a plus

Responsibilities

Understand a business challenge
Collaborate with business and other cross functional teams
Design a statistical or deep learning solution to find the needed answer to it
Develop the solution by yourself or guide another person to do it
Deliver the outcome into production
Keep a good governance of your work
Translate complex business requirements into scalable technical solutions meeting data warehousing/analytics design standards
Build solutions to improve efficiency
Implement leading data practices & standards
Collaborate with multiple multi-functional teams and work on solutions which has larger impact on Apple business
Communicate effectively, both written and verbal, with technical and non-technical multi-functional teams
Manage partner communication & project risks
Build GenAI-powered applications, including RAG pipelines, embeddings, vector databases, and semantic search integrated with structured/unstructured enterprise data
Build AI agents or agentic workflows (planning, memory, tool use, execution loops) using frameworks such as LangChain, LlamaIndex, or similar
Design and ship end-to-end data/AI products spanning backend APIs, data services, and front-end/UI layers
Translate ambiguous business problems into scalable technical designs
Take solutions from prototype to production with attention to governance, reliability, and observability
Work across cross-functional business and engineering teams, technical and non-technical alike
Build multi-agent systems/agentic workflows that autonomously coordinate retrieval, reasoning, and action steps across a data pipeline
Train and serve models using ML frameworks such as PyTorch or TensorFlow
Rapidly build internal data/AI-powered applications and dashboards
Brainstorm and ship POCs using AI/ML/GenAI services to solve new or existing enterprise problems
Manage partner communications and ambiguity in a fast-changing, matrixed environment