Hire LLM Engineers

How do you approach fine-tuning when data is limited?

Look for use of LoRA, prompt tuning, synthetic data generation, and transfer learning strategies.

Describe a time you debugged unexpected outputs in a custom LLM.

Expect prompt audits, dataset validation, and output sampling to trace model behavior.

How do you handle prompt injection vulnerabilities?

Expect sanitization, user input isolation, or system prompt reinforcement techniques.

What’s your strategy when inference latency becomes unacceptable?

Expect model quantization, caching, or moving to optimized runtimes like ONNX or Hugging Face Transformers.

How do you troubleshoot alignment issues in generation?

Look for use of reward modeling, preference learning, or instruction tuning to guide behavior.

Tell me about a time you customized an LLM for a unique use case.

Look for prompt design rigor, evaluation loop setup, and domain alignment.

Describe how you navigate tension between latency and performance in LLM deployments.

Expect experience with batching, caching, and infrastructure trade-offs.

How do you communicate LLM limitations to business stakeholders?

Look for clear examples, risk flags, and alignment with use case constraints.

What’s your approach to team feedback on prompt design experiments?

Expect openness, structured experimentation, and cross-functional collaboration.

Have you ever faced resistance to integrating LLMs into existing workflows?

Expect empathy, phased rollout, and measurable outcome framing.

• Inability to fine-tune or prompt large models effectively
• Overlooks context management and token limits
• Limited experience with embedding-based search
• Fails to align model behavior with user intent
• Over-reliance on default API behaviors