Start by clarifying requirements and constraints (e.g., latency, compliance, scale) and then walk through a high-level architecture before diving into each component. Focus on trade-offs, especially around LLM integration, data privacy, and scalability, and tie your decisions back to the fintech context.
Pro tip: Emphasize how you would handle sensitive financial data with LLMs, such as using on-prem or VPC-hosted models, data anonymization, and audit logging. Showing awareness of regulatory requirements (e.g., SOC2, GDPR) will set you apart.
Ask about expected scale (users, QPS), latency SLAs, compliance needs (e.g., data residency, encryption), and LLM provider options (open-source vs. API). This ensures your design meets the actual needs.
Sketch the main components: API gateway, chat service, LLM orchestration, data storage, and monitoring. Explain how they interact and the flow of a request from the frontend to the LLM and back.
Define schemas for conversations, messages, users, and audit logs. Discuss choices like SQL vs. NoSQL, vector databases for embeddings, and encryption at rest/in transit.
Describe how to scale each service (horizontal scaling, caching, async processing) and handle failures (retries, circuit breakers, fallback models). Mention rate limiting and load shedding.
Cover authentication, authorization, data anonymization, audit trails, and compliance with financial regulations. Explain how you protect sensitive data when using third-party LLMs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.