AI Platform Engineer

Design, build, and optimize largescale distributed data pipelines using Snowflake, SQL, and cloudbased data frameworks. Integrate LLMs, RAG pipelines, Agentic workflows, and multiagent execution patterns into data systems. Build infrastructure supporting agentic orchestration, including context handling, memory persistence, vector search integration, and multiagent communication (A2A/MCP). Collaborate with AI, software engineering, and ML teams to deliver endtoend AIpowered data solutions. Ensure scalable, secure, highly available data systems operating at Applescale. Support data ingestion, transformation, and evaluation frameworks powering foundation models and AI workflows. Build dashboards/insights using Tableau as needed for crossfunctional visibility.

Strong expertise in Snowflake & SQL, including data warehousing, distributed data processing, and performance optimization. Experience integrating AI/LLM systems, especially with Agentic frameworks (LangGraph, ADK, LangChain, LlamaIndex or similar). Handson experience building LLMpowered or agentic systems, including RAG pipelines and vector databases (FAISS, Milvus, Weaviate, Vespa, etc.). Strong programming experience in Python, with knowledge of distributed data systems and ML data flows. Understanding of largescale system architecture, data structures, concurrency, and multithreaded design. Experience processing data for ML applications at scale.

Preferred Qualifications Prior Apple experience or experience building systems at Applescale. Experience working with AgenttoAgent (A2A) protocols, context engineering, nondeterministic loop handling, and agentic memory. Experience with vector search, feature stores, and ML data pipelines. Experience integrating analytics tools such as Tableau for visual storytelling. Knowledge of secure data handling, promptinjection defense, and privacybydesign principles. Familiarity with cloud data ecosystems (Google Cloud Platform, AWS), Apache Beam, Kafka, Spark, or similar technologies.