Infosys Technologies Limited interview experience Real time questions & tips from candidates to crack your interview
Specialist Programmer
Infosys Technologies Limited
2 rounds | 6 Coding
problems
Selected Interview preparation journey
Application story
I applied for HackWithInfy through my college’s TPO (Training and Placement Office). The application was submitted as part of Infosys’s campus hiring initiative. After registering, I received the Online Assessment (OA) link, which I completed within the given timeline.
About a month after the OA, I was informed by my TPO that I had been shortlisted for the Specialist Programmer (SP) interview based on my performance. The interview was scheduled around 20 days later and was conducted virtually.
Around 20 days later, I got the result—I had been selected for the Specialist Programmer Role at Infosys, with a package of ₹9.5 LPA. I was later allotted Infosys Bhubaneswar as my preferred development centre.
A few days after that, I received an invite to attend the HackWithInfy Grand Finale Hackathon in Pune (Top 100 nationwide). Unfortunately, I couldn’t attend because I was already committed to JP Morgan & Chase’s Code for Good Hackathon, which was scheduled around the same time.
Why selected/rejected for the role?
I was selected for this role because I showcased a strong grasp of machine learning concepts, supported by impactful projects and research work. I was able to explain my solutions clearly, demonstrated good problem-solving skills during the coding round, and connected my academic work to real-world use cases. My ability to communicate technical ideas effectively and apply OOP and ML concepts practically played a key role in my selection.
Preparation
Duration: 15 days
Topics: Machine Learning, Deep Learning (CNN, Transfer Learning), OOPS, SQL, Graphs and DFS/BFS Problems
Tip 1: Focus on understanding your projects deeply and be ready to explain the problem, approach, and challenges clearly.
Tip 2: Practice medium to hard-level coding questions, especially those involving graphs, OOP, and system design patterns.
Tip 3: Revise core CS fundamentals like OOP, DBMS, and Operating Systems-they’re often asked in interviews.
Application process
Where: Campus
Eligibility: 6.5 CGPA, (Salary package: 9.5 LPA)
Tip 1: Highlight project outcomes and impact, not just the tools used-mention accuracy, performance improvements, or real-world relevance.
Tip 2: Keep it clean and concise-use bullet points, consistent formatting, and avoid clutter. Recruiters spend only a few seconds scanning it.
Interview rounds
01
Round
Hard
Online Coding Interview
Duration180 minutes
Interview date6 Apr 2025
Coding problem3
The environment was calm and professional. Since it was a virtual interview, I ensured a quiet setup with proper internet and no distractions. The interviewer maintained a friendly and conversational tone throughout, which helped ease any initial nervousness.
There weren’t any surprise activities, but the discussion was quite deep and project-focused. The interviewer gave me time to explain my projects in detail, especially the logic and challenges behind them. Towards the end, we also had a brief conversation about future goals and learning interests, which made the interview feel more personal and holistic.
1. Shortcut Maze Challenge
Hard
0/120
You're navigating a grid of size N x M. Each cell has a positive value that represents the cost to step into it. You start from the top-left corner of the grid and need to reach the bottom-right corner.
Movement is allowed only in two directions—right and down. However, a few special cells have access to hidden shortcuts that let you jump instantly to another location in the grid. These shortcuts are rare and limited—you can't use too many.
Problem approach
Tip 1: Track teleport uses separately using a 3D visited array
Tip 2: Treat teleport links as 0-cost edges in your graph.
Tip 3: Avoid recomputing paths on every query—use smart caching if possible.
2. The Grand Endeavor
Ninja
0/200
You have several projects, each with a duration, deadline, and reward (earned only if finished on time). You can do one project at a time without breaks, and you have a few helpers who can each reduce a project’s duration by half once. Choose which projects to do, in what order, and where to use your helpers to maximize your total reward before deadlines.
Problem approach
Tip 1:Sort projects by deadline to prioritize early completions.
Tip 2:Use 3D DP (project index, current time, helpers used) to track max rewards with/without helper usage.
Tip 3:At each step, choose to skip, do normally, or use a helper—update DP only if project finishes before its deadline.
3. Palindromic Subsequence Annihilation
Moderate
0/80
Given a string s, you can repeatedly remove any palindromic subsequence (not necessarily contiguous). Each removal gives you points equal to the square of the length of the subsequence removed. After each removal, the remaining characters shift left to form a new string. Determine the maximum total score achievable by removing subsequences in an optimal order.
Problem approach
Tip 1: Use dp[l][r] to store max score for substring s[l...r].
Tip 2: Try removing all palindromic subsequences s[i...j] within s[l...r].
Tip 3: Cache results to avoid redundant computations and speed up recursion.
02
Round
Hard
Face to Face
Duration105 minutes
Interview date15 May 2025
Coding problem3
The interview was scheduled in the afternoon and started on time. It lasted for around 1 hour 45 minutes.
Nothing unusual happened during the interview, but the discussion was very project and research oriented. The interviewer took genuine interest in my machine learning work, especially the CNN project and my published paper. Toward the end, we also briefly discussed my future plans and areas I want to grow in.
The interviewer was polite, attentive, and encouraging. They asked insightful follow-up questions, gave me time to think, and kept the conversation engaging throughout. Their balanced and supportive approach really helped me stay confident during the entire session.
1. Machine Learning
You are asked to design a basic machine learning pipeline structure using object-oriented programming principles. The system should support plugging in different models (like Random Forest, SVM, etc.) and allow easy training, evaluation, and prediction. The pipeline should also be extendable to support hyperparameter tuning in the future.
Problem approach
Tip 1: Start with a base abstract class Model that defines methods like train() and predict()
Tip 2: Follow SOLID principles: Especially Open/Closed Principle – your design should be open for extension (new models) but closed for modification.
Tip 3: Let your pipeline/controller class handle orchestration (training, evaluation), not model logic.
2. Find Number Of Islands
Moderate
34m average time
60% success
0/80
Asked in companies
You are given a 2-dimensional array/list having N rows and M columns, which is filled with ones(1) and zeroes(0). 1 signifies land, and 0 signifies water.
A cell is said to be connected to another cell, if one cell lies immediately next to the other cell, in any of the eight directions (two vertical, two horizontal, and four diagonals).
A group of connected cells having value 1 is called an island. Your task is to find the number of such islands present in the matrix.
Problem approach
Tip 1: Use DFS or BFS to traverse connected cells.
Tip 2: Increment the island count after each full DFS traversal.
Tip 3: Be careful with edge conditions—check grid bounds during recursion.
3. DBMS
Given a table Employee(salary INT), write a SQL query to find the 5th highest salary in the table. If there are fewer than 5 unique salaries, the query should return NULL or an empty result.
Problem approach
Tip 1: Use DISTINCT, ORDER BY, and LIMIT with OFFSET
Tip 2: Always check for duplicates—DENSE_RANK() is better when multiple employees can have the same salary.
Tip 3: For interviews, explain both methods and their trade-offs (especially if the interviewer asks about SQL window functions).
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