– Multi-agent systems – Orchestrating agent workflows, tool use, and state management.
– Advanced concepts (less common) – Fine-tuning smaller open-source models with LoRA, Retrieval-Augmented Fine-Tuning (RAFT), and custom embedding space alignment.
Example questions or scenarios:
– "How would you design a pipeline to ingest thousands of messy PDF and Word documents into a vector database for semantic search?"
– "Explain how you would handle query rewriting and query expansion in a multi-step RAG system to improve retrieval recall."
System Design for LLM Serving
Deploying language models at enterprise scale introduces unique challenges in latency, cost optimization, and concurrency. Interviewers will test your ability to design scalable infrastructure that balances performance with operational expenditure. Strong candidates can discuss serving frameworks, hardware constraints, and caching strategies with confidence.
Be ready to go over:
– System design for LLM serving – High-throughput architectures, load balancing, and GPU vs. CPU resource allocation.
– Latency optimization – Prompt caching, speculative decoding, and streaming responses.
– Reliability and monitoring – Handling API rate limits, fallback models, and telemetry tracking.
– Advanced concepts (less common) – Multi-tenant model virtualization, custom inference runtimes, and edge deployment of quantized models.
Example questions or scenarios:
– "Design a caching layer that reduces redundant LLM API calls across multiple concurrent user sessions while maintaining context freshness."
– "How would you architect a failover mechanism when your primary commercial LLM provider experiences an outage?"
Model Evaluation and Observability
Ensuring the safety, accuracy, and reliability of AI models is critical for enterprise adoption. Interviewers will assess your methodologies for testing model behavior, detecting hallucinations, and monitoring systems in production. Strong performance involves demonstrating a rigorous, metric-driven approach to quality assurance.
Be ready to go over:
– LLM evaluation – Automated evaluation frameworks, LLM-as-a-judge patterns, and benchmark datasets.
– Observability and tracing – Tracking token usage, latency bottlenecks, and intermediate agent reasoning steps.
– Guardrails and safety – Implementing input sanitization, output moderation, and bias detection.
- Advanced concepts (less common) – Adversarial prompt injection defense mechanisms and automated regression testing suites for prompt changes.
Example questions or scenarios:
– "How do you set up an automated evaluation pipeline to catch regressions when updating system prompts in a production RAG application?"
– "What metrics would you track to determine if a newly deployed model is outperforming the previous version in a customer support chatbot?"
