Recently, I had the opportunity to interview with **Quantrium** for an **Internship + PPO**, and I’m happy to share that I was selected 🎉. Overall, the interview process was well-structured, insightful, and focused on real-world understanding rather than rote learning. Below is a detailed breakdown of my experience. ## Interview Process Overview The selection process consisted of **four rounds**: 1. Resume Shortlisting 2. Online Assessment (OA) 3. Technical Round 1 (Senior Engineer) 4. Technical Round 2 (CTO & Project Manager) ## 1. Resume Shortlisting This was the initial screening round. Only candidates with **hands-on projects in the AI/ML domain** were shortlisted. Having relevant, well-documented projects played a crucial role here ## 2. Online Assessment (OA) The OA had **two sections**: * **MCQs** on AI/ML fundamentals * **Two coding problems** of medium difficulty (LeetCode-level) The coding questions tested logical thinking and problem-solving rather than obscure tricks. ## 3. Technical Round 1 (Senior Engineer) This round lasted about **1.5 hours** and was highly technical. ### Resume & Project Discussion It started with an introduction, followed by an in-depth discussion of my resume and projects. The interviewer focused on: * Why I chose specific tools/technologies * Trade-offs between different approaches * How design choices would impact scalability and performance **Tip:** Be honest, and know your projects deeply. You should be able to justify every design decision. ### AI/ML, DL, NLP & RAG The discussion then moved to AI/ML fundamentals, Deep Learning, NLP, and **RAG-based applications**. I was asked to: * Design a RAG system * Explain each pipeline component in detail * Diagnose issues like hallucination even when retrieval is correct * Discuss document fusion, query transformation, and other RAG challenges In ML, I was given a **problem statement** and asked which model I would choose (e.g., Logistic Regression vs Random Forest). The interviewer went deep into Random Forest to understand my **thinking process and model selection rationale**. ### OOPs & DSA In the final part: * OOP concepts in Python were discussed, and I wrote code * Two DSA problems were asked: * Two Sum * Kth Largest Element in an Array I was allowed to code in either Python or C++. The round ended with a Q&A, where I asked about the company’s tech stack, work culture, and interview feedback. ## 4. Technical Round 2 (CTO & Project Manager) This round was conducted **the very next day**. ### CTO Round (System Design & R&D Focus) The CTO focused more on **production readiness and system design** rather than implementation details. Questions included: * How to scale my project as users increase * My R&D experience and tools used * Model performance over time * Fine-tuning techniques and approaches * Ensuring correctness and reliability in RAG-based systems He also asked about **Explainable AI**. While I wasn’t deeply familiar with it, I answered based on intuition and logical reasoning, which he appreciated. ### Project Manager Round (Process & Behavioral) The Project Manager focused on teamwork and execution: * My experience working in groups * Project management approach (**Agile**) * Differences between Agile and Waterfall * Behavioral questions like: * Why Quantrium? * How I handle conflicting ideas within a team These were straightforward if you’ve worked in collaborative environments. At the end, I asked about: * Company culture * Growth trajectory * How I could contribute to the company’s growth in the next 6 months ## Final Thoughts The entire process tested **depth of knowledge, clarity of thought, system-level understanding, and teamwork skills**. It was less about memorization and more about how you approach real-world problems. I’m grateful for the experience and excited about what lies ahead at Quantrium 🚀.

##Online Assessment The first round consisted of an online assessment focused on core Artificial Intelligence, Machine Learning, and Deep Learning concepts. The questions were primarily easy to medium in difficulty and tested fundamental theoretical understanding. ##Technical Round 1 The first technical interview was conducted by a Senior AI Engineer at Yethi Consulting. This round focused on discussing projects listed on my resume, along with basic Data Structures and Algorithms (DSA). There was a strong emphasis on problem-solving and writing clean, correct Python code. Additional questions assessed my foundational programming and logical reasoning skills. ##Technical Round 2 The second technical interview was conducted by the AI Team Manager at Yethi Consulting. This round was more specialized and focused on Retrieval-Augmented Generation (RAG) and its real-world applications. The discussion included conceptual questions such as the motivation behind using RAG, its advantages over standalone LLMs, and practical implementation considerations. I was also asked about my familiarity with popular frameworks, particularly LangChain and LangGraph, and how they are used in building RAG-based systems.