Interview Guides
Databricks is scaling its AI Engineer motion around a unified lakehouse and GenAI stack: Apache Spark and PySpark for data processing, Delta Lake for storage, Unity Catalog for governance, Mosaic AI for model development, Databricks Foundation Model APIs and DBRX for inference, Databricks Vector Search for retrieval, Databricks Agent Framework for agent orchestration, MLflow and MLflow Agent Evaluation for evaluation, and Databricks Model Serving for deployment. You are a strategy lead supporting the GM for AI products. The field organization reports strong enterprise demand, but product and engineering capacity is constrained and leadership needs a recommendation on which customer-driven priorities should shape the next two planning cycles.
Over the last two quarters, Databricks has collected feedback from 180 enterprise opportunities and 42 active design partners building RAG and agent applications. The core question is how to influence product and engineering priorities based on customer needs and field learnings without fragmenting the roadmap. Leadership is debating whether to prioritize: (A) stronger enterprise governance and evaluation workflows across Unity Catalog, MLflow Agent Evaluation, and LLM-as-Judge; (B) lower-latency production serving and retrieval across Model Serving, Foundation Model APIs, DBRX, and Vector Search; or (C) faster developer adoption through simplified Agent Framework templates and PySpark/Spark-based reference architectures.
| Signal | Current Data |
|---|---|
| AI pipeline | $410M qualified pipeline tied to AI Engineer use cases over next 12 months |
| Opportunity mix | 45% RAG copilots, 30% agentic workflow automation, 15% model customization/fine-tuning with Mosaic AI, 10% other |
| Top customer blockers | 34% evaluation/groundedness concerns, 29% governance/security concerns, 23% latency/cost concerns, 14% developer complexity |
| Win-rate gap | Deals with production-ready eval + governance story close at 31% vs 18% without it |
| Engineering capacity | 120 effective engineer-months available over next 2 quarters; major initiative estimates: Governance/Eval 55, Serving/Retrieval 65, Developer Experience 40 |
Additional field notes:
You are preparing a recommendation for Databricks executive staff.
