Deep-dive guides for every topic asked in AI/ML interviews. Real questions, key concepts, and model answers.
Large Language Models power modern AI products. Interviews test your understanding of transformer internals, generation strategies, prompt engineering, and production challenges like hallucination and latency.
Retrieval-Augmented Generation is the dominant pattern for grounding LLMs in real data. Interviews test chunking strategies, embedding models, vector search, re-ranking, and evaluation.
AI Agents use LLMs to plan and execute actions. Interviews cover ReAct, tool use, memory management, multi-agent coordination, and evaluation challenges.
Fine-tuning adapts pre-trained models for specific tasks. Interviews focus on parameter-efficient methods (LoRA, QLoRA), RLHF, DPO, data preparation, and evaluation.
MLOps covers the full production ML lifecycle. Interviews test model serving, monitoring, CI/CD, feature stores, drift detection, and infrastructure design.
ML System Design combines software architecture with machine learning. Interviews test recommendation systems, search, real-time ML, and production ML infrastructure at scale.
The transformer architecture underpins virtually all modern AI. Interviews go deep on attention variants, positional encoding, layer normalization, and efficiency improvements.
Python proficiency is essential for ML engineers. Interviews test data structures, PyTorch internals, async programming, memory management, and production-quality code.
Vector databases enable semantic search and similarity at scale. Interviews cover indexing algorithms, distance metrics, approximate nearest neighbor, and production trade-offs.
Behavioral interviews test how you handle real situations. Use the STAR method (Situation, Task, Action, Result) for every answer. Prepare 5-6 strong stories covering multiple leadership principles.
Neural networks, backpropagation, CNNs, RNNs, optimization algorithms and advanced architectures. Core for any ML engineer role.
Core ML algorithms, evaluation metrics, feature engineering and ensemble methods. Essential for all ML engineer and data scientist interviews.
Probability, distributions, hypothesis testing, Bayesian inference and A/B testing for ML engineers.
CNNs, object detection, segmentation, vision transformers and image processing fundamentals.
Text processing, word embeddings, sequence models, BERT and language understanding fundamentals.