AI Interview for AI/ML Engineers: Transformers, RAG & Agentic Systems
Prepare for applied scientist and generative AI engineering loops. Defend model architectures, mathematical formulas, inference optimizations, and production RAG pipelines under live voice interrogation.
Core AI/ML Interview Dimensions Evaluated
Transformers & Sequence Models
Scaled dot-product attention, FlashAttention kernel memory fusion, RoPE positional encodings, and KV-cache optimization during autoregressive generation.
Advanced RAG & Vector Search
Hybrid search (dense vector embeddings + BM25 lexical with Reciprocal Rank Fusion), cross-encoder reranking, and semantic chunking boundary strategies.
LoRA, QLoRA & Alignment
Parameter-efficient fine-tuning low-rank matrices, 4-bit NormalFloat quantization, and preference alignment via DPO (Direct Preference Optimization).
Agentic Loops & Tool Calling
ReAct loop design, JSON schema constrained decoding, multi-step error recovery, infinite loop termination guards, and LLM evaluation benchmarks.
Frequently Asked Questions
What topics are covered in the AI/ML interview track?
The track covers Transformer Attention (MHA, GQA, RoPE), Fine-Tuning (LoRA, QLoRA, RLHF, DPO), RAG architectures (hybrid retrieval, rerankers, semantic chunking), and Agentic tool use (ReAct loops, structured decoding).
Can I practice defending my personal LLM or ML projects?
Yes. Veyra extracts your exact project claims, prompting you to defend your chunking strategies, embedding dimensions, loss functions, evaluation benchmarks, and GPU inference latencies.
Does Veyra test ML system design or mathematical foundations?
Both. You will be evaluated on the mathematical rationale behind loss functions and attention scaling, as well as production ML system design (caching embeddings, handling model drift, and optimizing KV-cache throughput).
Prepare for Modern AI Engineering Loops
Test your grasp of transformers, embeddings, and agentic workflows before your hiring round.