AI/ML Engineer Interview Guide: Technical & System Design

AI/ML Engineer Interview Guide 2026: LLMs, RAG, and Production ML

AI/ML engineering interviews in 2026 have shifted dramatically toward LLMs, prompt engineering, RAG systems, and ML operations. Traditional ML topics (regression, classification) remain important, but LLM-related questions now appear in 60%+ of ML interviews at top companies.

The 2026 ML interview has three pillars: 1) Classical ML (statistics, model selection, evaluation), 2) Deep Learning & LLMs (transformers, fine-tuning, prompt engineering, RAG), 3) ML Systems (training pipelines, serving infrastructure, monitoring, A/B testing).

LLM-Specific Topics

• Transformer Architecture — Self-attention mechanism, positional encoding, key/query/value
• Fine-Tuning — LoRA, QLoRA, PEFT approaches, when to fine-tune vs prompt engineer
• RAG — Retrieval-Augmented Generation: embedding models, vector databases, chunking strategies
• Evaluation — BLEU, ROUGE, human evaluation, LLM-as-judge approaches
• Deployment — vLLM, TensorRT-LLM, model quantization, batching strategies

Related: ML interview guide, data science tips. Practice: AissenceAI.