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 🚀.