Databricks behavioural interview template for AI Engineer (Lakehouse/ML & LLM delivery)

Purpose: Assess how an AI Engineer operates in Databricks’ high-ownership, data-driven, customer-obsessed culture while shipping ML/LLM solutions on the Lakehouse. The bar emphasizes end-to-end delivery, collaborative problem solving with field/product/infra partners, and rigorous decision-making backed by data. Structure (60 minutes): - 0–3 min: Introductions and context. Interviewer sets expectations: deep STAR stories with measurable impact; follow-up drilling is expected. - 3–13 min: Ownership & end-to-end delivery. Prompt: “Tell me about a time you shipped an ML/LLM feature to production under ambiguity.” Drill into scoping, data/feature pipelines, model serving, latency/cost targets, rollout/rollback, and what changed post-launch. - 13–23 min: Let-the-data-decide decisions. Prompt: “Describe a high-stakes trade-off you made (quality vs latency/cost/security) and how you validated it.” Look for experiment design, offline/online metrics, A/B or interleaving tests, MLflow-style tracking, drift monitoring, and post-launch guardrails. - 23–33 min: Customer impact and cross-functional collaboration. Prompt: “Walk through a tough enterprise customer situation and how you turned it around.” Expect partnering with Solutions Architects/Support/PM, handling SLAs/SEVs, crisp status comms, and aligning on success criteria. - 33–43 min: Reliability at scale & incident learning. Prompt: “Tell me about a production incident in data/serving (e.g., cost spike, skew, drift, quota limits) and what you changed.” Probe for root cause, blameless postmortem actions, runbooks, alerting/SLOs, and continuous improvement. - 43–53 min: Innovation, openness, and bar-raising. Prompt: “Example where you advanced the state of practice (e.g., RAG evaluation framework, safety/guardrails, feature store/Delta Lake patterns, contributions to OSS like Spark/MLflow/Delta).” Seek pragmatic impact over novelty, knowledge sharing, and mentoring. - 53–60 min: Candidate questions and wrap-up. What interviewers evaluate (signals): - Ownership and bias to action: drove outcomes without perfect inputs; raised the bar for quality, security, and reliability. - Data-first judgment: clear hypotheses, metrics (quality, latency, cost, safety), experiment discipline, and willingness to change course when data contradicts intuition. - Customer obsession: translates ambiguous stakeholder needs into measurable success; balances enterprise constraints (governance, privacy) with speed. - Collaboration at scale: partners effectively with field teams, platform, and PM; communicates crisply; influences without authority. - Production mindset for AI: understands the full lifecycle—data readiness, evaluation, safety/guardrails, observability, rollback plans, and cost/performance management in cloud environments. Evaluation rubric (behavioral-only): - Strong hire: Repeated, quantified outcomes; articulates trade-offs with metrics; shows Lakehouse-scale thinking; clear learning loops; demonstrates mentorship and bar-raising. - Lean hire: Solid execution with some metrics; minor gaps in experimentation rigor or stakeholder alignment, but shows growth mindset. - No hire: Vague impact, lacks metrics/experiments; blames others; weak collaboration; research-only mindset without production rigor. Databricks-specific focus areas to probe: - Building on open formats and services (e.g., Delta-like data contracts, feature stores, vector indexing) and how choices impacted governance, cost, and performance. - ML/LLM evaluation beyond accuracy (safety, reliability, hallucination controls, prompt/response logging, canarying, red-teaming, and feedback loops). - Large-scale data considerations: skew, cost controls, caching/optimization strategies, and incident readiness for batch/streaming/serving. - Partnering with field-facing teams on Fortune 500 use cases and closing-the-loop from support tickets/SEVs into platform improvements. Preparation guidance for candidates (shared context): bring 2–3 deep stories with numbers (e.g., p50/p95 latency, $ cost deltas, win-rate lift, error-rate change, drift metrics), be ready to whiteboard decision trees and rollback criteria, and expect iterative why/how probes.