Recruitment at top IT companies is evolving. Gone are the days when every candidate sat across a human panel. Today, companies like LTI Mindtree are introducing AI-based interviews to streamline hiring. These interviews test not just your technical knowledge but also your communication skills, problem-solving approach, and ability to think under pressure.

If you are preparing for the LTI AI Interview 2025, this blog will walk you through the structure, common questions, and smart preparation strategies.

What is an AI-Based Interview?

An AI interview is a virtual assessment where you interact with a system rather than a human recruiter. The system records your responses, analyzes speech clarity, grammar, and content, and evaluates your coding and technical answers.

The goal is to ensure fairness, reduce interviewer bias, and assess a large number of candidates efficiently.

Flow of the LTI AI Interview

Step 1: Resume Upload & Introduction

  • Upload your resume to the platform.

  • Start with a self-introduction: your background, education, and career aspirations.

  • AI may pick keywords from your resume and frame questions.

Possible Prompts:

  • Tell me about yourself.

  • Walk me through your projects.

  • What role did you play in your project team?

  • What challenges did you face, and how did you overcome them?

Step 2: Resume-Based Questions

These questions check how well you know your own work. They often cover:

  • Project details: technology stack, your contribution.

  • Teamwork: resolving conflicts, handling disagreements.

  • Client-facing skills: how you deal with dissatisfaction.

  • Learning from academics: how your degree motivated you.

Example Questions:

  • How did your degree help you in building real-world skills?

  • What technologies have you used so far?

  • Can you describe a conflict in your team and how you resolved it?

Step 3: Technical Questions

This section tests your conceptual knowledge and applied problem-solving.

Topics to Prepare:

  • Core AI/ML concepts: CNNs, GNNs, normalization, hyperparameters.

  • CS fundamentals: recursion, call stack, acyclic graphs, debugging.

  • Time complexity: definition, examples, calculations.

  • Adaptability: Describe a technology you implemented without prior experience.

Sample Prompt:

  • Explain the concept of hyperparameters in machine learning.

  • What is debugging, and how do you approach it?

  • How does recursion work with a call stack?

Step 4: Coding Questions

The coding part is lighter compared to a full coding round. It usually contains:

  • Simple coding questions (arrays, strings, logic building).

  • Role-specific problems depending on your applied position.

  • You may be asked to explain your approach verbally while coding.

Tips:

  • Focus on writing clean, readable code.

  • Mention time complexity when explaining your solution.

  • Use standard languages like C, C++, Java, or Python.

Step 5: Concluding Questions

At the end, the AI may ask:

  • Do you have any questions about the position?

  • What are your final thoughts about this interview?

  • How was your overall experience?

This is a chance to show curiosity and professionalism. Always prepare a thoughtful question about the role or growth opportunities.

How to Prepare for LTI AI Interviews

  1. Know Your Resume: AI tools scan for keywords. Be prepared for detailed project-based questions.

  2. Revise CS Fundamentals: Recursion, complexity analysis, debugging, and graph concepts are common.

  3. Brush Up on AI Basics: CNNs, GNNs, normalization, and hyperparameters should be in your fingertips.

  4. Practice Coding Aloud: Since you may need to explain your solution, practice thinking and speaking at the same time.

  5. Communication Skills: The system judges grammar, tone, and fluency. Record yourself and review to improve clarity.