I applied online. I interviewed at QuickReply.ai (Gurgaon, Haryana) in Feb 2026
Interview
First I cleared AI based Interview and then I completed their assignment. After that i got a call from HR and interview was scheduled. It was good learning experience.
I hope next time I can clear such opportunities.
The most important thing is to discuss your doubts with the interviewer.
Interview questions [1]
Question 1
You are building an API Rate Limiter for a backend service.
Each user is allowed to make at most K requests within a rolling window of T seconds.
boolean allowRequest(userId, timestamp)
Rules
userId is a string.
timestamp is an integer representing seconds.
Return true if the request is allowed.
Return false if the user has already made K requests in the last T seconds.
Example
K = 3, T = 10
allowRequest("user1", 1) → true
allowRequest("user1", 2) → true
allowRequest("user1", 3) → true
allowRequest("user1", 4) → false
allowRequest("user1", 11) → true
Constraints
Up to 1 million users
Requests arrive in non-decreasing timestamp order
Solution must be efficient in both time and memory
I applied online. I interviewed at QuickReply.ai (Gurgaon, Haryana) in Mar 2026
Interview
The interview consisted of two rounds focused on problem-solving and practical development skills.
The first question was a frontend task. I was asked to build a React component that displays a list of users and implement a search input to filter the users by name or email as the user types.
The second question was a data structures problem where I was asked to design and implement an LRU (Least Recently Used) cache. The expected solution involved using a HashMap along with a Doubly Linked List to achieve O(1) time complexity for both get and put operations.
Interview questions [1]
Question 1
The first question was a frontend task. I was asked to build a React component that displays a list of users and implement a search input to filter the users by name or email as the user types.
The second question was a data structures problem where I was asked to design and implement an LRU (Least Recently Used) cache. The expected solution involved using a HashMap along with a Doubly Linked List to achieve O(1) time complexity for both get and put operations.
