Priyanka Todkar

Priyanka TodkarNov 2, 2026

AI/ML Engineer Interview Questions Preparing for roles such as: - AI/ML Engineer - Generative AI Engineer - AI Agent Engineer Here are 8 important interview questions to challenge your knowledge: 1. Multi-Agent Systems Describe a complex multi-agent or LLM-based solution you designed and deployed. What architectural decisions and production challenges did you face? 2. Agentic AI Architecture How would you design an agent graph using Google ADK and LangGraph for multi-step tool-calling workflows, including failures, retries, ambiguous results, and infinite-loop prevention? 3. Python & Algorithms How would you implement Levenshtein distance using dynamic programming? How would you identify matching product-name pairs within a given distance threshold? 4. Retrieval-Augmented Generation (RAG) What happens when retrieved context is too broad or too narrow? How would you optimize chunking, embeddings, Top-K, similarity thresholds, and reranking? 5. LLM Evaluation How would you design an evaluation pipeline for multi-turn conversations? Which metrics would you use to measure tool-calling accuracy and final-answer quality? 6. Model Context Protocol (MCP) In an MCP-based architecture, what tool information is exposed to the LLM, and how does the model decide which tool to call? 7. AI Observability & Evaluation How would you structure and evaluate tool inputs, outputs, execution traces, and feedback data to improve future model performance? 8. Reusable MCP Architecture If multiple agents need access to the same tool, how would you design the MCP/tool layer to make it reusable, secure, and efficient? 𝗜 𝗵𝗮𝘃𝗲 𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱 𝗙𝗼𝗿𝗴𝗲 — 𝗣𝘆𝘁𝗵𝗼𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗠𝗮𝘀𝘁𝗲𝗿𝘆 𝗞𝗶𝘁. 𝗚𝗲𝘁 𝗙𝗼𝗿𝗴𝗲 𝗵𝗲𝗿𝗲 : https://lnkd.in/dGwqD97A Share your thoughts in the comments! #AI #MachineLearning #GenAI #AgenticAI #RAG #MCP #LangGraph #GoogleADK #MLOps #DataScience #AIEngineering #InterviewPreparation

Priyanka TodkarOct 31, 2026

𝗪𝗵𝘆 𝗗𝗼 𝗟𝗟𝗠𝘀 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗲? And more importantly, how do you reduce hallucinations in production? LLMs generate likely next tokens. They don't automatically verify that every statement is factually correct. Here are 3 techniques to reduce hallucinations. 1. Retrieval-Augmented Generation (RAG) Retrieve relevant documents and instruct the model to ground its answer in that evidence. Cost: Retrieval infrastructure, document processing, and added latency. 2. Structured Outputs and Validation Use schemas, constraints, and application-level checks to validate generated responses. Cost: Additional implementation effort, and schema validity still doesn't guarantee factual correctness. 3. Evaluation and Verification Build test datasets, check claims against trusted sources, and evaluate answer quality before deployment. Cost: Evaluation pipelines, human review, and ongoing maintenance. You can also instruct the model to acknowledge uncertainty or abstain when evidence is insufficient. But no single technique eliminates hallucinations completely. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗴𝗼𝗮𝗹 𝗶𝘀𝗻'𝘁 𝘁𝗼 𝗺𝗮𝗸𝗲 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝘀𝗼𝘂𝗻𝗱 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝘁. It's to build a system that produces verifiable answers—and knows when it doesn't have enough evidence. Interview question: How would you measure hallucination rates in a customer-facing RAG application? 𝗜 𝗵𝗮𝘃𝗲 𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱 𝗙𝗼𝗿𝗴𝗲 — 𝗣𝘆𝘁𝗵𝗼𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗠𝗮𝘀𝘁𝗲𝗿𝘆 𝗞𝗶𝘁. 𝗚𝗲𝘁 𝗙𝗼𝗿𝗴𝗲 𝗵𝗲𝗿𝗲 : https://lnkd.in/dGwqD97A #LLM #AIEngineering #RAG #GenerativeAI #AIEvaluation #ResponsibleAI