Databricks is a leading data and AI platform used by enterprises to build analytics, ML, and generative AI applications on a unified lakehouse. Its customers range from digital-native startups to highly regulated global enterprises, and many are adopting Apache Spark, Delta Lake, Unity Catalog, Mosaic AI, Model Serving, Vector Search, and Foundation Model APIs at different levels of maturity.
Databricks wants AI Engineers to act as trusted technical advisors, not just solution implementers. Today, customer conversations are often reactive and tool-centric: one retail customer asks about a RAG chatbot with Databricks Vector Search, a healthcare customer wants governance with Unity Catalog, and a financial services customer is evaluating DBRX through Databricks Foundation Model APIs but is blocked on evaluation and hallucination concerns. Internal feedback shows that advisory quality varies significantly by engineer, time-to-first-production use case is too slow, and customers struggle to connect technical architecture choices to business outcomes.
You are the product manager for a new advisory experience inside Databricks that helps field AI Engineers consistently guide customers from discovery to production design. The experience could include opinionated playbooks, domain-specific solution templates, evaluation workflows using MLflow Agent Evaluation and LLM-as-Judge, reference architectures for spark/pyspark + Delta Lake pipelines, and recommendation logic for when to use Mosaic AI, Agent Framework, Model Serving, or Foundation Model APIs.