Infosys Machine Learning Engineer Interview Questions + Guide in 2025

Infosys is a global leader in technology services and consulting, helping clients navigate their digital transformation journeys with innovative solutions.

As a Machine Learning Engineer at Infosys, you will play a crucial role in developing and implementing machine learning models that enhance the capabilities of various applications and services. Your key responsibilities will include designing algorithms, training models on large datasets, and optimizing their performance to ensure scalability and efficiency. A deep understanding of programming languages such as Python or R, along with proficiency in machine learning frameworks like TensorFlow or PyTorch, is essential. You should also possess strong analytical skills and be comfortable working with data preprocessing and feature engineering techniques.

In line with Infosys's commitment to innovation and excellence, ideal candidates will demonstrate a passion for solving complex problems and a collaborative spirit, as you will often work in cross-functional teams to deliver impactful solutions. This guide aims to equip you with the knowledge and insights necessary to excel in your interview, helping you to confidently articulate your experience and align it with Infosys's values and objectives.

The interview process for a Machine Learning Engineer at Infosys is structured to assess both technical skills and cultural fit within the organization. The process typically unfolds in several key stages:

1. Initial HR Screening

The first step in the interview process is an initial phone screening with an HR representative. This conversation usually lasts around 30 minutes and focuses on your background, experiences, and motivations for applying to Infosys. The HR interviewer will likely ask you to elaborate on your resume and may take notes to ensure they capture your responses accurately. This is also an opportunity for you to ask questions about the company culture and the role itself.

2. Technical Interview

Following the HR screening, candidates typically participate in a technical interview, which may be conducted via video conferencing. This interview is designed to evaluate your technical expertise in machine learning concepts, algorithms, and programming skills. Expect to discuss your previous projects, problem-solving approaches, and possibly tackle coding challenges or case studies relevant to machine learning applications.

3. Panel Interview

The next stage often involves a panel interview, where you will meet with multiple team members, including senior engineers and project managers. This round assesses both your technical knowledge and your ability to collaborate within a team. Questions may cover a range of topics, including data preprocessing, model evaluation, and deployment strategies. Behavioral questions may also be included to gauge how you handle challenges and work in a team environment.

4. Final Interview

The final interview is typically with a senior leader or manager within the organization. This round focuses on your long-term career goals, alignment with Infosys's values, and your potential contributions to the team. It may also include discussions about your understanding of the industry and how you stay updated with the latest trends in machine learning.

As you prepare for these interviews, it's essential to be ready for a variety of questions that will test your technical and interpersonal skills.

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

Prepare for a Structured Interview Process

Infosys tends to follow a structured interview format, which means you should be ready to discuss your background and experiences in a clear and concise manner. Be prepared to articulate your previous projects, the technologies you used, and the impact of your work. Practice summarizing your experiences in a way that highlights your problem-solving skills and technical expertise, as this will be crucial in demonstrating your fit for the Machine Learning Engineer role.

Be Ready for Technical Assessments

Expect to face technical questions that assess your understanding of machine learning algorithms, data preprocessing, and model evaluation techniques. Brush up on key concepts such as supervised vs. unsupervised learning, overfitting, and feature selection. Additionally, be prepared to solve coding problems on the spot, so practice coding in languages relevant to the role, such as Python or R. Familiarize yourself with libraries like TensorFlow or PyTorch, as these may come up during discussions.

Communicate Clearly and Confidently

Given the feedback from previous candidates, it’s essential to communicate your thoughts clearly during the interview. Practice articulating your ideas and solutions in a structured manner. If you’re asked to explain a complex concept, break it down into simpler parts. This not only showcases your understanding but also demonstrates your ability to communicate effectively, which is vital in a collaborative environment like Infosys.

Follow Up Professionally

If you experience any delays or issues during the interview process, such as a missed call or lack of communication, don’t hesitate to follow up with the HR representative. A polite and professional follow-up can demonstrate your enthusiasm for the role and your proactive nature. It’s also a good opportunity to reiterate your interest in the position and ask any lingering questions you may have.

Embrace the Company Culture

Infosys values innovation, collaboration, and continuous learning. During your interview, express your enthusiasm for working in a team-oriented environment and your commitment to staying updated with the latest advancements in machine learning. Share examples of how you have collaborated with others in the past or how you have pursued learning opportunities, as this aligns well with the company’s culture.

By following these tips, you can present yourself as a strong candidate for the Machine Learning Engineer role at Infosys. Good luck!

In this section, we’ll review the various interview questions that might be asked during an interview for a Machine Learning Engineer position at Infosys. The interview process will likely focus on your technical expertise in machine learning algorithms, data processing, and software engineering principles, as well as your ability to work collaboratively in a team environment.

Machine Learning

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

Understanding the fundamental concepts of machine learning is crucial, and this question tests your grasp of basic principles.

How to Answer

Clearly define both supervised and unsupervised learning, providing examples of each. Highlight the scenarios in which you would use one over the other.

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, like clustering customers based on purchasing behavior.”

2. Describe a machine learning project you have worked on. What challenges did you face?

This question assesses your practical experience and problem-solving skills in real-world applications.

How to Answer

Discuss a specific project, focusing on the problem you were solving, the approach you took, and the challenges you encountered, along with how you overcame them.

Example

“I worked on a project to predict customer churn for a subscription service. One challenge was dealing with imbalanced data. I implemented techniques like SMOTE to generate synthetic samples and improved the model's performance significantly.”

3. How do you handle overfitting in a machine learning model?

This question evaluates your understanding of model evaluation and optimization techniques.

How to Answer

Explain the concept of overfitting and discuss various strategies to mitigate it, such as cross-validation, regularization, or pruning.

Example

“To handle overfitting, I use techniques like cross-validation to ensure the model generalizes well to unseen data. Additionally, I apply regularization methods like L1 and L2 to penalize overly complex models, which helps maintain a balance between bias and variance.”

4. What metrics do you use to evaluate the performance of a machine learning model?

This question tests your knowledge of model evaluation and the importance of selecting appropriate metrics.

How to Answer

Discuss various metrics relevant to the type of problem (classification, regression, etc.) and explain why they are important.

Example

“For classification tasks, I typically use accuracy, precision, recall, and F1-score to evaluate model performance. For regression, I prefer metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to assess how well the model predicts continuous outcomes.”

5. Can you explain the concept of feature engineering and its importance?

This question assesses your understanding of data preprocessing and its impact on model performance.

How to Answer

Define feature engineering and discuss its significance in improving model accuracy and interpretability.

Example

“Feature engineering involves creating new input features from existing data to improve model performance. It’s crucial because well-engineered features can capture underlying patterns that raw data may not reveal, leading to better predictive accuracy.”

Statistics & Probability

1. What is the Central Limit Theorem and why is it important?

This question tests your foundational knowledge of statistics and its application in machine learning.

How to Answer

Explain the Central Limit Theorem and its implications for statistical inference and model building.

Example

“The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the original distribution. This is important because it allows us to make inferences about population parameters even when the underlying data is not normally distributed.”

2. How do you handle missing data in a dataset?

This question evaluates your data preprocessing skills and understanding of data integrity.

How to Answer

Discuss various strategies for handling missing data, including imputation techniques and the importance of understanding the nature of the missing data.

Example

“I handle missing data by first analyzing the pattern of missingness. Depending on the situation, I might use imputation techniques like mean or median substitution, or more advanced methods like K-Nearest Neighbors. If the missing data is substantial, I may also consider removing those records if it doesn’t significantly impact the dataset.”

3. Explain the difference between Type I and Type II errors.

This question assesses your understanding of hypothesis testing and its implications in decision-making.

How to Answer

Define both types of errors and provide examples to illustrate their significance in a practical context.

Example

“A Type I error occurs when we reject a true null hypothesis, while a Type II error happens when we fail to reject a false null hypothesis. For instance, in a medical test, a Type I error could mean falsely diagnosing a patient with a disease, while a Type II error could mean missing a diagnosis when the disease is present.”

4. What is the purpose of A/B testing?

This question evaluates your knowledge of experimental design and its application in data-driven decision-making.

How to Answer

Explain the concept of A/B testing and its importance in evaluating the effectiveness of changes in a controlled manner.

Example

“A/B testing is used to compare two versions of a variable to determine which one performs better. It’s crucial for making data-driven decisions, such as optimizing a website’s layout or a marketing campaign, by measuring user engagement and conversion rates.”

5. How do you assess the correlation between two variables?

This question tests your understanding of statistical relationships and their implications in modeling.

How to Answer

Discuss methods for assessing correlation, such as Pearson’s correlation coefficient, and the importance of understanding the relationship between variables.

Example

“I assess the correlation between two variables using Pearson’s correlation coefficient, which measures the strength and direction of a linear relationship. A coefficient close to 1 or -1 indicates a strong correlation, while a value near 0 suggests little to no correlation, which is essential for feature selection in modeling.”