Generative AI is one of the fastest-growing fields in 2025. Companies are hiring Generative AI Engineers to design LLM pipelines, fine-tune models, and build real-world AI applications. Preparing well for interviews is key to landing these roles.
Here are the top 15 Generative AI Engineer interview questions and tips to crack them.
Technical Questions
- What’s the difference between GPT-style models and diffusion models?
- How do you fine-tune a large language model with limited data?
- Explain prompt engineering – what techniques improve response quality?
- How would you design a RAG (Retrieval-Augmented Generation) pipeline?
- What are common challenges in training generative models at scale?
- How do you evaluate the quality of generative outputs?
- Compare LoRA vs full fine-tuning.
- How do you mitigate hallucinations in LLMs?
- Explain the concept of embeddings and their role in semantic search.
- How would you deploy a generative model in production with low latency?
Behavioral & Problem-Solving Questions
- Tell me about a time you built an AI system that directly impacted business.
- How do you stay updated on fast-moving AI research?
- What ethical considerations must be made when deploying generative AI?
- How do you work with cross-functional teams (PMs, designers, data scientists)?
- Imagine you’re tasked with reducing inference cost by 40%. How would you approach it?
Quick Preparation Tips
- Stay updated with OpenAI, Anthropic, Google DeepMind releases.
- Brush up on transformers, embeddings, fine-tuning methods.
- Practice explaining complex AI concepts simply.
- Use platforms like giveinterview.com to simulate real interviews.
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