1. The Evolution of AI Engineering Interviews
Interviews in 2026 test beyond simple prompt design. Companies want engineers who understand latency optimization, token economics, evaluation benchmarks (LLM-as-a-Judge), and hybrid retrieval architectures.
2. Core AI & LLM Technical Scenarios
Q1: How do you optimize Retrieval-Augmented Generation (RAG) for high precision?
- Chunking Strategy: Use recursive semantic chunking with overlapping windows (e.g. 512 tokens with 64-token overlap) rather than fixed character splits.
- Hybrid Search: Combine Dense Vector Search (cosine similarity on embedding vectors) with Sparse BM25 keyword matching via Reciprocal Rank Fusion (RRF).
- Re-ranking: Pass top 25 retrieved passages through a cross-encoder re-ranker (e.g. Cohere / BGE-Reranker) before feeding the top 5 into the LLM context.
Q2: Full Fine-Tuning vs Parameter-Efficient Fine-Tuning (LoRA / QLoRA): When to use which?
Answer: Full fine-tuning updates all model weights, requiring massive VRAM (e.g. 8x 80GB A100s for a 70B model). LoRA (Low-Rank Adaptation) freezes base weights and trains small low-rank decomposition matrices ($A imes B$), training < 1% of total parameters with 95%+ of full fine-tuning performance.
3. Match AI Engineer ATS Keywords with Vaylo AI
Scan your resume against real Applied AI and ML job descriptions for free with Vaylo AI Free ATS Resume Checker.