Salesforce AI Engineer Case Interview: Designing Einstein-powered CRM Experiences on Customer 360

This case mirrors how Salesforce evaluates AI Engineers building GenAI features within the Einstein 1 Platform for large, multi-tenant enterprises. You’ll scope and design an AI capability that augments Sales or Service Cloud workflows (e.g., an Opportunity/Case Copilot that summarizes signals, drafts actions, and recommends next best steps) while upholding Salesforce’s core value of Trust. What you’ll tackle: - Problem discovery with a hypothetical enterprise customer: clarify business goals (e.g., reduce case handle time, improve forecast accuracy), success metrics (CSAT, time-to-first-response, win rate), and constraints (compliance, data residency, SSO/SCIM, encryption at rest/in transit). - End-to-end architecture on Salesforce: how the solution grounds LLM outputs in CRM data via Data Cloud/CRM objects; retrieval design (indexing object records, notes, emails, knowledge articles), prompt strategy (system + retrieval-augmented prompts), and use of Einstein Trust Layer for data grounding, PII handling, audit, and zero data retention with external model providers. - Model strategy: when to use Salesforce-provided models vs external LLMs (via Model Builder) and how to evaluate them; fallback/abstain behavior, hallucination mitigation, content safety, and guardrails aligned with enterprise policies. - Platform realities: multi-tenancy and metadata-driven design, API/throughput and governor limits, async processing patterns (queues/events), Shield encryption implications, and tenancy-aware feature flags. Consider Slack integration for agent/AE workflows and Tableau for analytics where appropriate. - Quality/evaluation: offline/online evals, golden sets, retrieval precision/recall, groundedness/hallucination rate, human-in-the-loop review, prompt/version management, and monitoring (latency, cost per task, model drift, safety incidents). - Rollout & ops: packaging for multiple orgs, permissioning and field-level security, sandbox strategy, A/B rollouts, observability, cost controls, and failure modes. Interview flow (typical): 1) 5 min: brief intro and context. 2) 10–15 min: discovery—ask clarifying questions and align on business outcomes and constraints. 3) 30–35 min: whiteboard the architecture and data/ML approach; discuss trade-offs and platform considerations unique to Salesforce (Customer 360, Data Cloud, Trust Layer, limits, multi-tenancy). 4) 10–15 min: risks, safety/compliance, evaluation plan, and incremental rollout. 5) 5–10 min: Q&A and reflection on v1 vs v2 roadmap. What interviewers assess (Salesforce style): customer-centric thinking, bias to trust/safety by design, pragmatic model choices, clear trade-offs under enterprise constraints, and ability to collaborate cross-functionally with PMs/solution architects while communicating simply and inclusively.