Razorpay Machine Learning Engineer Interview Questions + Guide in 2025

Razorpay is a leading fintech company enabling businesses to accept, process, and disburse payments seamlessly.

The Machine Learning Engineer (MLE) role at Razorpay is pivotal for the development and deployment of advanced machine learning models that address various complex challenges in the fintech sector. Key responsibilities include collaborating with data scientists and engineers to create production-quality models for tasks such as optimizing transaction costs, implementing natural language processing for address correction, and leveraging computer vision for auto-verifications. The ideal candidate should possess a solid foundation in machine learning techniques, including regression, classification, and clustering, as well as a strong proficiency in Python and experience with big data tools like Spark.

A great fit for this role will have a deep understanding of deploying machine learning algorithms in production environments, preferably using tools like Flask and MLFlow, alongside familiarity with cloud platforms such as AWS, Microsoft Azure, or GCP. Excellent communication skills are also essential for keeping stakeholders informed and engaged throughout the development process.

This guide will help you prepare for your interview by providing insights into the skills and experiences Razorpay values, allowing you to tailor your responses and demonstrate your fit for the Machine Learning Engineer position.

The interview process for a Machine Learning Engineer at Razorpay is structured to assess both technical expertise and cultural fit within the team. Here’s what you can expect:

1. Initial Screening

The process begins with an initial screening, typically conducted by a recruiter over a phone call. This conversation lasts about 30 minutes and focuses on your background, experience, and motivation for applying to Razorpay. The recruiter will also gauge your understanding of the role and the company culture, ensuring that you align with Razorpay's values and mission.

2. Technical Assessment

Following the initial screening, candidates will undergo a technical assessment, which may be conducted via a coding platform or through a video call. This assessment is designed to evaluate your proficiency in algorithms, Python programming, and machine learning concepts. You may be asked to solve coding problems that require you to demonstrate your understanding of basic and advanced machine learning techniques, as well as your ability to implement solutions in a production environment.

3. Onsite Interviews

The onsite interview consists of multiple rounds, typically ranging from three to five interviews with various team members, including data scientists and engineers. Each interview lasts approximately 45 minutes and covers a mix of technical and behavioral questions. You will be expected to discuss your past projects, particularly those involving productionizing machine learning models, and how you have collaborated with cross-functional teams. Additionally, you may be asked to present a case study or a project that showcases your problem-solving skills and technical expertise.

4. Final Interview

The final interview is often with a senior leader or manager within the data science team. This round focuses on your long-term vision, leadership potential, and how you can contribute to Razorpay's goals. Expect discussions around your approach to mentoring others, your communication skills, and how you handle challenges in a team setting.

As you prepare for these interviews, it’s essential to familiarize yourself with the specific skills and technologies relevant to the role, particularly in algorithms and Python programming. Next, let’s delve into the types of questions you might encounter during the interview process.

Here are some tips to help you excel in your interview.

Understand the Role's Technical Demands

As a Machine Learning Engineer at Razorpay, you will be expected to have a strong grasp of algorithms and machine learning techniques. Focus on brushing up your knowledge of regression, classification, and clustering methods, as well as model performance metrics like AUC, ROC, precision, and recall. Be prepared to discuss how you have applied these techniques in real-world scenarios, particularly in production environments.

Showcase Your Python Proficiency

Python is a critical skill for this role, so ensure you are comfortable with its libraries and frameworks commonly used in machine learning, such as TensorFlow, PyTorch, and Scikit-learn. Be ready to demonstrate your coding skills through practical exercises or whiteboard challenges. Highlight any projects where you have successfully implemented machine learning models using Python.

Familiarize Yourself with Big Data Tools

Razorpay values experience with big data tools like Spark and platforms such as Databricks. If you have worked with these technologies, prepare to discuss specific projects where you utilized them. If not, consider familiarizing yourself with their functionalities and how they can be applied to machine learning tasks, as this knowledge will be beneficial during the interview.

Prepare for Productionization Discussions

The ability to productionize machine learning models is crucial for this role. Be ready to discuss your experience with deploying models using tools like Flask, MLFlow, or Seldon. Share examples of how you have collaborated with engineers to ensure smooth deployment and integration of machine learning solutions into existing systems.

Emphasize Collaboration and Communication Skills

Razorpay values teamwork and effective communication. Be prepared to discuss how you have worked collaboratively with data scientists, engineers, and stakeholders in previous roles. Highlight your ability to keep stakeholders informed about project progress and any challenges faced. This will demonstrate your fit within Razorpay's culture of collaboration and transparency.

Align with Company Culture

Razorpay emphasizes a fun and growth-oriented work environment. During your interview, convey your enthusiasm for the role and the company. Share examples of how you have contributed to a positive team culture in the past, and express your eagerness to learn and grow within the organization. This will help you resonate with the interviewers and show that you are a good cultural fit.

Prepare Thoughtful Questions

Finally, prepare insightful questions to ask your interviewers. Inquire about the specific challenges the team is currently facing, the technologies they are excited about, or how they measure success in their machine learning initiatives. This not only shows your genuine interest in the role but also helps you assess if Razorpay is the right fit for you.

By following these tips, you will be well-prepared to showcase your skills and align with Razorpay's values, giving you a competitive edge in your interview. Good luck!

Razorpay Machine Learning Engineer Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Razorpay Machine Learning Engineer interview. The interview will focus on your technical expertise in machine learning, algorithms, and programming, as well as your ability to work collaboratively in a team environment. Be prepared to discuss your experience with productionizing machine learning models and your familiarity with various tools and techniques.

Machine Learning Fundamentals

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial for this role.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight the types of problems each approach is best suited for.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns or groupings, like clustering customers based on purchasing behavior.”

2. What are some common metrics used to evaluate machine learning models?

This question assesses your understanding of model performance evaluation.

How to Answer

Mention key metrics such as accuracy, precision, recall, F1 score, AUC, and ROC. Explain when to use each metric based on the problem context.

Example

“Common metrics include accuracy for overall correctness, precision for the relevance of positive predictions, and recall for the ability to identify all relevant instances. For imbalanced datasets, I often rely on the F1 score, which balances precision and recall, and AUC-ROC to evaluate the model's performance across different thresholds.”

3. Describe a machine learning project you worked on from start to finish.

This question allows you to showcase your practical experience.

How to Answer

Outline the problem, your approach, the algorithms used, and the results. Emphasize your role in the project and any challenges faced.

Example

“I worked on a recommendation system for an e-commerce platform. I started by gathering and preprocessing data, then implemented collaborative filtering and content-based filtering algorithms. After evaluating the model using precision and recall, we achieved a 20% increase in user engagement, which was a significant success for the team.”

4. How do you handle overfitting in machine learning models?

This question tests your knowledge of model optimization techniques.

How to Answer

Discuss techniques such as cross-validation, regularization, and pruning. Explain how you would apply these methods in practice.

Example

“To combat overfitting, I often use cross-validation to ensure the model generalizes well to unseen data. Additionally, I apply regularization techniques like L1 and L2 to penalize overly complex models. In tree-based models, I also consider pruning to simplify the model without sacrificing performance.”

5. What is the role of feature engineering in machine learning?

This question assesses your understanding of data preparation.

How to Answer

Explain the importance of feature engineering in improving model performance and how it can involve creating new features or transforming existing ones.

Example

“Feature engineering is crucial as it directly impacts the model's ability to learn. For instance, in a customer churn prediction model, I created features like average purchase frequency and customer lifetime value, which significantly improved the model's predictive power.”

Algorithms and Programming

1. Can you describe a situation where you had to optimize an algorithm for performance?

This question evaluates your problem-solving skills and technical expertise.

How to Answer

Provide a specific example where you identified a performance bottleneck and the steps you took to optimize the algorithm.

Example

“I was tasked with optimizing a recommendation algorithm that was running too slowly. I profiled the code to identify bottlenecks and implemented a more efficient data structure, which reduced the computation time by 50%. This allowed us to provide real-time recommendations to users.”

2. What is your experience with Python libraries for machine learning?

This question assesses your familiarity with essential tools.

How to Answer

Mention libraries such as Scikit-learn, TensorFlow, and PyTorch, and describe how you have used them in your projects.

Example

“I have extensive experience with Scikit-learn for traditional machine learning tasks, and I’ve used TensorFlow for building deep learning models. For instance, I developed a convolutional neural network using TensorFlow to classify images, which improved our image recognition accuracy significantly.”

3. How do you ensure the reproducibility of your machine learning experiments?

This question tests your understanding of best practices in machine learning.

How to Answer

Discuss the importance of version control, documentation, and using tools like Docker or MLFlow for tracking experiments.

Example

“I ensure reproducibility by using version control for my code and maintaining detailed documentation of my experiments. Additionally, I utilize MLFlow to track parameters, metrics, and artifacts, which allows me to reproduce results easily and share findings with my team.”

4. Explain the concept of ensemble learning and its benefits.

This question assesses your knowledge of advanced machine learning techniques.

How to Answer

Define ensemble learning and discuss its advantages, such as improved accuracy and robustness.

Example

“Ensemble learning combines multiple models to improve overall performance. Techniques like bagging and boosting help reduce variance and bias, respectively. For example, using Random Forest, which is an ensemble of decision trees, often leads to better accuracy than a single tree model.”

5. What is your experience with deploying machine learning models in production?

This question evaluates your practical experience with deployment.

How to Answer

Discuss the tools and frameworks you have used for deployment, such as Flask, Docker, or cloud services.

Example

“I have deployed machine learning models using Flask to create REST APIs, allowing other applications to access the model's predictions. Additionally, I’ve utilized AWS services for scaling and managing the deployment, ensuring that the models can handle production-level traffic efficiently.”