Software Engineer : Data & AI
Apple · Software and Services
Apply ↗Skills
["AWS""Azure""Cassandra""Deep Learning""GCP""Go""Java""Kafka""PyTorch""Python""RAG""React""SQL""Scala""Snowflake""Spark""TensorFlow"]Technology stack
{"programming_languages": ["Python""Java""Go""Scala""SQL"]"frameworks": ["React""Kafka""Spark""PyTorch""TensorFlow"]"databases": ["Cassandra""Snowflake"]"cloud": ["AWS""Azure""GCP"]"infrastructure": []"data_tools": []"ai_ml": ["PyTorch""TensorFlow""Deep Learning""RAG"]"other_tools": []}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.
Requirements
["Bachelor's degree or equivalent experience, with 4+ years in data engineering and applied AI/ML engineering", "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", "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", "Experience building AI agents or agentic workflows (planning, memory, tool use, execution loops) using frameworks such as LangChain, LlamaIndex, or similar", "Preferred: Experience building multi-agent systems/agentic workflows that autonomously coordinate retrieval, reasoning, and action steps across a data pipeline", "Preferred: 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", "Preferred: Experience with modern front-end/full-stack frameworks (e.g., React, Streamlit, FastAPI) for rapidly building internal data/AI-powered applications and dashboards", "Preferred: 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", "Preferred: 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", "Preferred: Ability to translate ambiguous business problems into scalable technical designs, and take solutions from prototype to production with attention to governance, reliability, and observability", "Preferred: Strong collaboration and communication skills to work across cross-functional business and engineering teams, technical and non-technical alike", "Preferred: Prior experience in enterprise domains such as Retail, Finance, Sales, Marketing, or Operations analytics is a plus"]
Roles & responsibilities
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