Tech Mahindra
Multinational IT services and consulting company.
4 Rounds
~21 Days
Medium
The Interview Loop
Recruiter Screen (30 min)
Standard fit check, behavioral questions, and resume overview.
Technical Loop (3-4 Rounds)
Deep dive into domain knowledge, coding, and system design.
Interview Question Bank
Machine Learning Engineer
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Behavioral
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medium
Tell me about a time you had to explain a complex machine learning concept to a non-technical client stakeholder.
#Stakeholder Management
#Communication
Machine Learning Engineer
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Behavioral
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medium
Describe a situation where your model performed well in training but failed in production. How did you handle it?
#Troubleshooting
#Experience
Machine Learning Engineer
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Behavioral
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easy
How do you prioritize tasks when working on multiple client deliverables with tight deadlines?
#Prioritization
#Consulting
Machine Learning Engineer
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Behavioral
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medium
Tell me about a time you disagreed with a senior engineer or architect on a technical approach.
#Teamwork
#Professionalism
Machine Learning Engineer
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Behavioral
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easy
Why do you want to join Tech Mahindra, and how do you see yourself contributing to our AI/ML practice?
#Motivation
#Company Knowledge
Machine Learning Engineer
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Coding
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medium
Write a Python function to find the longest substring without repeating characters.
#Python
#Strings
#Sliding Window
Machine Learning Engineer
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Coding
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easy
Write a SQL query to find the second highest salary from an Employee table.
#SQL
#Aggregations
#Subqueries
Machine Learning Engineer
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Coding
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easy
Using Pandas, how would you group a dataset by 'customer_id' and find the top 5 customers by total purchase amount?
#Python
#Pandas
#Data Wrangling
Machine Learning Engineer
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Coding
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hard
Implement a basic K-Means clustering algorithm from scratch in Python.
#Python
#Machine Learning
#Math
Machine Learning Engineer
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Coding
•
medium
Write a Python function to compute the cosine similarity between two lists of numbers without using external libraries like scikit-learn.
#Python
#Linear Algebra
Machine Learning Engineer
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Coding
•
easy
Write a Python script to reverse a singly linked list.
#Data Structures
#Linked Lists
Machine Learning Engineer
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System Design
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hard
Design a predictive maintenance system for a telecom client's cell towers.
#IoT
#Time Series
#Architecture
Machine Learning Engineer
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System Design
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hard
Design a real-time fraud detection system for a banking client.
#Real-time Processing
#Fraud Detection
Machine Learning Engineer
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System Design
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hard
How would you design a scalable recommendation engine for an e-commerce platform?
#Recommendation Systems
#Scalability
Machine Learning Engineer
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System Design
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medium
Design an MLOps pipeline using AWS SageMaker or Azure ML for continuous training and deployment.
#Cloud
#CI/CD
#Pipeline
Machine Learning Engineer
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System Design
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medium
Design a document extraction system using OCR and NLP to process client invoices.
#Computer Vision
#NLP
#OCR
Machine Learning Engineer
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System Design
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medium
How would you deploy an ML model as a REST API using FastAPI and Docker?
#Deployment
#Docker
#FastAPI
Machine Learning Engineer
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Technical
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medium
Explain the difference between Random Forest and Gradient Boosting. Which would you use for a highly imbalanced dataset and why?
#Ensemble Methods
#Classification
Machine Learning Engineer
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Technical
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medium
How do you handle missing values in a dataset where 40% of the data in a critical feature is missing?
#Data Cleaning
#Imputation
Machine Learning Engineer
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Technical
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hard
Explain the architecture of a Transformer model. How does the self-attention mechanism work?
#NLP
#Transformers
#Attention
Machine Learning Engineer
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Technical
•
medium
What is the Curse of Dimensionality, and how does Principal Component Analysis (PCA) help mitigate it?
#Dimensionality Reduction
#PCA
Machine Learning Engineer
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Technical
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easy
How do you evaluate a classification model on a highly imbalanced dataset?
#Metrics
#Classification
Machine Learning Engineer
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Technical
•
medium
Explain L1 and L2 regularization. When would you prefer Lasso over Ridge?
#Regularization
#Linear Models
Machine Learning Engineer
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Technical
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medium
What are the vanishing and exploding gradient problems in RNNs, and how do LSTMs solve them?
#RNN
#LSTM
#Gradients
Machine Learning Engineer
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Technical
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hard
How does XGBoost handle missing values internally?
#XGBoost
#Tree Models
Machine Learning Engineer
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Technical
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hard
Explain the concept of Retrieval-Augmented Generation (RAG). How would you implement it for a client's internal knowledge base?
#LLMs
#RAG
#Vector Databases
Machine Learning Engineer
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Technical
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easy
What are the differences between bagging and boosting?
#Ensemble Methods
Machine Learning Engineer
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Technical
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hard
How do you optimize the inference speed of a Large Language Model for a production application?
#LLMs
#Optimization
#Deployment
Machine Learning Engineer
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Technical
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medium
Explain the difference between generative and discriminative models.
#Theory
#Modeling
Machine Learning Engineer
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Technical
•
medium
How do you detect and handle data drift in a deployed machine learning model?
#Monitoring
#Data Drift
Machine Learning Engineer
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Technical
•
medium
What is SMOTE and how does it work? What are its limitations?
#Imbalanced Data
#SMOTE
Machine Learning Engineer
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Technical
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easy
Explain the bias-variance tradeoff. How do you know if your model is overfitting?
#Theory
#Model Evaluation
Machine Learning Engineer
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Technical
•
medium
What are the key differences between PyTorch and TensorFlow? Why might you choose one over the other for a production deployment?
#Frameworks
#PyTorch
#TensorFlow
Machine Learning Engineer
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Technical
•
medium
Explain the concept of cross-validation. Why is time-series cross-validation different from standard K-Fold?
#Validation
#Time Series
Machine Learning Engineer
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Technical
•
easy
What is the role of an activation function in a neural network? Explain ReLU and its variants.
#Neural Networks
#Activation Functions
Difficulty Radar
Based on recent AI-sourced data.
Meet Your Interviewers
The "Standard" Interviewer
Senior EngineerFocuses on core competencies, system constraints, and clear communication.
SimulateUnwritten Rules
Think Out Loud
Always explain your thought process before writing code or drawing architecture.