This is the round I'm walking into, not one I've already done, so I can't say how it went.
Start by clarifying the enterprise workflow automation use case and its constraints (e.g., data sensitivity, latency, integration points). Then propose a layered architecture: a foundation model layer, an orchestration layer for workflow logic, and an integration layer with enterprise systems, while discussing trade-offs around accuracy, cost, and security.
Pro tip: Emphasize the importance of human-in-the-loop validation and fallback mechanisms for critical workflows, and discuss how you would measure success (e.g., time saved, error reduction) to show business impact.
Ask questions to understand the specific workflow, data sources, latency needs, compliance requirements, and success metrics. This ensures the design is tailored and addresses real pain points.
Outline a modular architecture with components: model serving (e.g., fine-tuned LLMs), workflow orchestration (e.g., state machines, DAGs), integration APIs (e.g., REST, webhooks), and monitoring. Explain how components interact.
Discuss choices like model size vs. latency, fine-tuning vs. prompt engineering, on-prem vs. cloud deployment, and synchronous vs. asynchronous processing. Justify decisions based on requirements.
Describe measures for data privacy (e.g., encryption, access control), auditability, and error handling (e.g., retries, human-in-the-loop). Highlight enterprise-grade concerns.
Propose metrics (e.g., accuracy, latency, cost per task) and a feedback loop for continuous improvement. Mention A/B testing and monitoring for drift.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.