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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 🚀.
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 🚀.
## Interview Overview I attended the interview for the AI Internship Programme at the City Union Bank Centre of Excellence, SASTRA University. The interview was around 15 minutes long and focused mainly on my technical background, machine learning project, SQL knowledge, and basic banking concepts. The interview was conversational and the interviewer asked questions based on the skills and projects mentioned in my profile. This made it important to understand my projects properly rather than simply memorizing theoretical answers. ## My Project Discussion A major part of the interview was based on my Telecom Customer Churn Prediction project. I was asked to explain the project, the problem I was trying to solve, the dataset, preprocessing steps, and the machine learning models I used. I explained how I performed exploratory data analysis, identified missing values, handled categorical variables through encoding, prepared the data for modelling, and divided it into training and testing datasets. I had experimented with multiple machine learning algorithms, including: - Logistic Regression - Decision Tree - Random Forest I also discussed the evaluation metrics used to compare the models, including accuracy, precision, recall, and F1-score. One important takeaway from the project discussion was that you should be able to explain not only what model you used, but also WHY you used it and how you decided which model performed better. ## SQL and Technical Questions The interview also covered SQL and basic technical concepts. Revising SQL before the interview was useful, especially concepts such as filtering data, aggregation, grouping, joins, and writing queries to retrieve meaningful information from tables. The interview also tested my understanding of basic machine learning concepts rather than only asking me to write code. For students preparing for similar interviews, I would recommend being comfortable with: - Python fundamentals - Pandas and NumPy - SQL queries - Machine learning fundamentals - Data preprocessing - Classification algorithms - Model evaluation metrics - Basic statistics - Exploratory Data Analysis ## Banking Concepts Since the internship was related to banking and customer analytics, I was also expected to have an understanding of basic banking concepts. The preparation included concepts related to customer analysis, banking products, transactions, and how data analytics can be used to understand customer behaviour. Having a basic understanding of banking terminology is useful when applying for AI or Data Science roles in the BFSI domain. Technical knowledge alone may not be enough when the internship involves solving business problems using financial or customer data. ## What I Learned One of the biggest lessons from the interview was the importance of understanding your own resume. If you mention a machine learning project, be prepared to explain the complete workflow: 1. What problem are you solving? 2. What dataset did you use? 3. What preprocessing did you perform? 4. How did you handle missing values? 5. How did you encode categorical variables? 6. Which models did you try? 7. Why did you choose those models? 8. Which evaluation metrics did you use? 9. Which model performed best and why? 10. What could you improve in the future? The same applies to programming languages and tools mentioned in your resume. Interviewers can ask questions from any skill you list. ## Preparation Tips For students preparing for AI/ML internships, I would recommend focusing on fundamentals instead of trying to learn too many advanced topics at the last minute. Revise Python, SQL, machine learning algorithms, EDA, preprocessing, and evaluation metrics. At the same time, understand your projects deeply and practice explaining them in simple language. For banking-related AI roles, spend some time learning basic banking concepts and customer analytics terminology as well. Most importantly, don't just memorize definitions. Try to understand how a concept would actually be used to solve a real-world problem. ## Final Takeaway The interview was a good opportunity to connect my academic knowledge and machine learning project experience with a real-world banking use case. It also showed me that for internship interviews, strong fundamentals and the ability to clearly explain your own work are extremely important. If you are preparing for a similar AI/Data Science internship, focus on your projects, Python, SQL, ML fundamentals, and domain-specific concepts. Be honest about what you know, and make sure you can confidently explain everything mentioned on your resume.
## Interview Overview I attended the interview for the AI Internship Programme at the City Union Bank Centre of Excellence, SASTRA University. The interview was around 15 minutes long and focused mainly on my technical background, machine learning project, SQL knowledge, and basic banking concepts. The interview was conversational and the interviewer asked questions based on the skills and projects mentioned in my profile. This made it important to understand my projects properly rather than simply memorizing theoretical answers. ## My Project Discussion A major part of the interview was based on my Telecom Customer Churn Prediction project. I was asked to explain the project, the problem I was trying to solve, the dataset, preprocessing steps, and the machine learning models I used. I explained how I performed exploratory data analysis, identified missing values, handled categorical variables through encoding, prepared the data for modelling, and divided it into training and testing datasets. I had experimented with multiple machine learning algorithms, including: - Logistic Regression - Decision Tree - Random Forest I also discussed the evaluation metrics used to compare the models, including accuracy, precision, recall, and F1-score. One important takeaway from the project discussion was that you should be able to explain not only what model you used, but also WHY you used it and how you decided which model performed better. ## SQL and Technical Questions The interview also covered SQL and basic technical concepts. Revising SQL before the interview was useful, especially concepts such as filtering data, aggregation, grouping, joins, and writing queries to retrieve meaningful information from tables. The interview also tested my understanding of basic machine learning concepts rather than only asking me to write code. For students preparing for similar interviews, I would recommend being comfortable with: - Python fundamentals - Pandas and NumPy - SQL queries - Machine learning fundamentals - Data preprocessing - Classification algorithms - Model evaluation metrics - Basic statistics - Exploratory Data Analysis ## Banking Concepts Since the internship was related to banking and customer analytics, I was also expected to have an understanding of basic banking concepts. The preparation included concepts related to customer analysis, banking products, transactions, and how data analytics can be used to understand customer behaviour. Having a basic understanding of banking terminology is useful when applying for AI or Data Science roles in the BFSI domain. Technical knowledge alone may not be enough when the internship involves solving business problems using financial or customer data. ## What I Learned One of the biggest lessons from the interview was the importance of understanding your own resume. If you mention a machine learning project, be prepared to explain the complete workflow: 1. What problem are you solving? 2. What dataset did you use? 3. What preprocessing did you perform? 4. How did you handle missing values? 5. How did you encode categorical variables? 6. Which models did you try? 7. Why did you choose those models? 8. Which evaluation metrics did you use? 9. Which model performed best and why? 10. What could you improve in the future? The same applies to programming languages and tools mentioned in your resume. Interviewers can ask questions from any skill you list. ## Preparation Tips For students preparing for AI/ML internships, I would recommend focusing on fundamentals instead of trying to learn too many advanced topics at the last minute. Revise Python, SQL, machine learning algorithms, EDA, preprocessing, and evaluation metrics. At the same time, understand your projects deeply and practice explaining them in simple language. For banking-related AI roles, spend some time learning basic banking concepts and customer analytics terminology as well. Most importantly, don't just memorize definitions. Try to understand how a concept would actually be used to solve a real-world problem. ## Final Takeaway The interview was a good opportunity to connect my academic knowledge and machine learning project experience with a real-world banking use case. It also showed me that for internship interviews, strong fundamentals and the ability to clearly explain your own work are extremely important. If you are preparing for a similar AI/Data Science internship, focus on your projects, Python, SQL, ML fundamentals, and domain-specific concepts. Be honest about what you know, and make sure you can confidently explain everything mentioned on your resume.
# The selection process for Deloitte typically focuses on core data structures, algorithmic optimization, and database architecture. ## Selection Process: * PPT Presentation * Online Coding Test * Technical Interview 1/ 2/3 (Based on the interview performance) * HR Interview ## Recruiting Tips From developing a standout resume to putting your best foot forward in the interview, we want you to feel prepared and confident as you explore opportunities at Hashedin Technologies. Here are some recruiting tips from our Team * Be Prepared to discuss your approach to challenges and problem-solving, explaining your thought process and decision making. * Be ready to discuss personal projects in detail, highlighting your role and contributions to showcase your technical skills and communication abilities. * Research the company thoroughly to understand its values, mission, culture and recent developments, demonstrating your genuine interest. * During problem-solving or case study interviews, focus on demonstrating your unique approach and thought process by showcasing your innovative thinking. ## Round Details ## 1. Coding Round - **Number of Questions**: 3 Questions - **Breakdown**: 1 Easy, 2 Medium - **Key Problems**: - String search: Check if a given string is present in an array of strings. - Array Optimization: Find all pairs $(i, j)$ where $arr[i] > arr[j] * 3$. - *Note: Requires an optimized $O(n \log n)$ approach to pass large test cases.* ## 2. Technical Interview (One-to-One) - **Number of Questions**: 3–5 Questions - **Difficulty**: Medium - **Topics**: - **Heaps**: Implementation or priority queue applications. - **Two Pointers**: Used for array/string manipulation. - **Linked Lists**: Detecting cycles and finding the intersection point of two lists. ## 3. Database Design Round - **Focus**: System architecture and data modeling. - **Scenario**: Airport Booking System. - **Requirements**: - Draw/Explain ER Diagrams. - Perform Schema Design. - Write complex SQL queries based on the designed schema. --- ## **Tips** * **Communication is Key**: If you explain your logic confidently and correctly, interviewers may waive the requirement to write the full code for certain questions. * **Optimization**: For array pair problems, think beyond nested loops; consider modified Merge Sort or Fenwick tree logic for large constraints.
# The selection process for Deloitte typically focuses on core data structures, algorithmic optimization, and database architecture. ## Selection Process: * PPT Presentation * Online Coding Test * Technical Interview 1/ 2/3 (Based on the interview performance) * HR Interview ## Recruiting Tips From developing a standout resume to putting your best foot forward in the interview, we want you to feel prepared and confident as you explore opportunities at Hashedin Technologies. Here are some recruiting tips from our Team * Be Prepared to discuss your approach to challenges and problem-solving, explaining your thought process and decision making. * Be ready to discuss personal projects in detail, highlighting your role and contributions to showcase your technical skills and communication abilities. * Research the company thoroughly to understand its values, mission, culture and recent developments, demonstrating your genuine interest. * During problem-solving or case study interviews, focus on demonstrating your unique approach and thought process by showcasing your innovative thinking. ## Round Details ## 1. Coding Round - **Number of Questions**: 3 Questions - **Breakdown**: 1 Easy, 2 Medium - **Key Problems**: - String search: Check if a given string is present in an array of strings. - Array Optimization: Find all pairs $(i, j)$ where $arr[i] > arr[j] * 3$. - *Note: Requires an optimized $O(n \log n)$ approach to pass large test cases.* ## 2. Technical Interview (One-to-One) - **Number of Questions**: 3–5 Questions - **Difficulty**: Medium - **Topics**: - **Heaps**: Implementation or priority queue applications. - **Two Pointers**: Used for array/string manipulation. - **Linked Lists**: Detecting cycles and finding the intersection point of two lists. ## 3. Database Design Round - **Focus**: System architecture and data modeling. - **Scenario**: Airport Booking System. - **Requirements**: - Draw/Explain ER Diagrams. - Perform Schema Design. - Write complex SQL queries based on the designed schema. --- ## **Tips** * **Communication is Key**: If you explain your logic confidently and correctly, interviewers may waive the requirement to write the full code for certain questions. * **Optimization**: For array pair problems, think beyond nested loops; consider modified Merge Sort or Fenwick tree logic for large constraints.
I'm working as AI/ML intern at Yethi Consulting
##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.
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