Kayhut Software Engineer Interview: Questions & Prep (2026)

Kayhut is actively hiring, with 14 open Software Engineer roles on the market right now. Candidates report the process typically runs across a few rounds covering coding, system design, and behavioural questions. Some roles include a brief HR or culture-fit call at the start or close.

The broader context matters too. As of July 2026, knok jobradar tracked 5,395 Software Engineer openings across India, with Bangalore leading at 776 roles. Getting into an actively hiring company during a strong hiring window is a genuine opportunity, and showing up prepared makes the difference.

Based on publicly reported candidate experiences, these questions come up most often in Kayhut Software Engineer interviews:

1. Walk me through a project you owned from design to deployment. What decisions did you make and why?
2. How do you debug a production issue you have never seen before? Take me through your process step by step.
3. Design a URL shortener service. How would you scale it to handle very high traffic?
4. Given an array of integers, find two numbers that add up to a target sum. What is your approach and what is the time complexity?
5. When would you choose a NoSQL database over a relational one? Give me a real example from your work.
6. How do you maintain code quality when your team is shipping features under deadline pressure?
7. Tell me about a time you disagreed with a technical decision made by your team. How did you handle it?
8. What is your experience with microservices? What tradeoffs have you seen in real projects?
9. Design a basic REST API for a food delivery app. What endpoints, what data models, what edge cases?
10. Describe a performance bottleneck you identified and fixed. How did you measure success?
11. What does a good code review look like to you? What do you actually comment on?
12. What have you learned or built in the last six months that genuinely excites you?

Q: Tell me about a time you fixed a critical production issue under pressure.

*Situation:* Our payment service began returning errors on a Friday evening, affecting a large number of users trying to complete checkout.

*Task:* I was the on-call engineer that weekend and needed to identify the root cause and restore service as quickly as possible.

*Action:* I pulled the application logs immediately and noticed a spike in database timeouts that began right after a deployment two hours earlier. I rolled back that deployment, confirmed the error rate dropped back to normal, and opened a detailed post-mortem ticket. Before closing the incident, I identified a missing index as the likely underlying cause and scheduled a fix for the next release.

*Result:* Service was restored well within our SLA window. The post-mortem led to a new pre-deploy checklist for database migrations, and we had zero similar incidents for the rest of that quarter.

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Q: Describe a project where you improved system performance significantly.

*Situation:* Our internal reporting dashboard was taking an unacceptably long time to load for large accounts, and customer success was escalating complaints every week.

*Task:* I was asked to investigate and resolve the slowness within two sprints.

*Action:* I profiled the slow queries and found three N+1 query patterns causing the bottleneck. I rewrote those queries with proper joins, added caching for report aggregations that rarely change, and moved heavy computation to a background job with a loading indicator in the UI so users had feedback.

*Result:* Load times dropped dramatically for the same data sets. Customer success reported a clear reduction in complaints, and the approach became a reference example in our internal engineering documentation.

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Q: Tell me about a time you pushed back on a technical decision.

*Situation:* My tech lead proposed storing all user activity logs in a single relational table. I was concerned this design would not hold up once we reached meaningful data volumes.

*Task:* I needed to raise the concern clearly and constructively, without creating conflict, and ideally offer a practical alternative.

*Action:* I ran a quick benchmark using realistic data volumes, shared the results in the next design review, and suggested using a time-series store for activity data while keeping the relational store for user profiles. I framed it as 'here is what I found' rather than 'your approach is wrong.'

*Result:* The team appreciated the data-backed framing and we adopted the hybrid model. Over the following year, the system handled growing data load comfortably without needing a costly migration.

STAR for behavioural questions: Structure every 'tell me about a time' answer as Situation, Task, Action, Result. Keep Situation and Task brief and spend most of your speaking time on Action and Result. Interviewers want to hear what you personally did, not what the team collectively achieved. Use 'I' when describing your actions and 'we' only when crediting shared outcomes.

Think-aloud for coding questions: Before writing any code, restate the problem in your own words and ask about edge cases. Candidates report that interviewers value your reasoning process as much as the final solution. Say out loud: 'I am thinking about a hash map here because the lookup is O(1)...' rather than going silent and producing code with no explanation.

Component-first thinking for system design: Start by clarifying scale and constraints before drawing anything. Then define the main components and how they communicate. For each tradeoff (SQL vs NoSQL, sync vs async, monolith vs microservices), name the tradeoff explicitly and explain your reasoning. Interviewers typically want to hear you work through options, not jump straight to a conclusion.

PREP for opinion questions: Point, Reason, Example, Point again. When asked what you think about a technology or approach, state your view first, explain why, give a concrete example from your experience, then restate your position briefly. This keeps answers tight and confident without rambling.

Kayhut, like most product companies actively scaling their engineering team, is looking for engineers who can own problems end to end. Based on what candidates typically share, a few themes stand out consistently.

Ownership mindset: Interviewers want to see that you do not wait for someone else to define the next step. In your behavioural answers, make your personal decisions visible. Crediting the team for results is fine, but your specific actions need to be clear and traceable.

Clear communication: Engineering at a product company means working across product managers, QA engineers, and sometimes client-facing teams. Interviewers pay close attention to how clearly you explain a technical idea to someone outside your domain. Practice giving a one-sentence plain-English summary of any concept before going deeper.

Practical engineering judgment: The interview is not just a theory check. Interviewers want to know whether you make sensible tradeoffs under real-world constraints (time pressure, scale requirements, team capacity). When answering design or debugging questions, always anchor your choices to practical context rather than textbook ideals.

Curiosity and growth: Expect at least one question about what you have learned or built recently. Have a genuine, specific answer ready. A side project, an open-source contribution, or a recent course you completed shows initiative that goes beyond your day job.

Week 1: Data structures and algorithms

Revise the core structures (arrays, linked lists, trees, hash maps, heaps) and the algorithms that accompany them (binary search, BFS, DFS, basic dynamic programming). Pick one practice platform and stick with it. Consistency across a few weeks outperforms jumping between resources.

Week 2: System design fundamentals

Study the standard building blocks: load balancers, caches, message queues, relational and NoSQL databases. Then practice designing a few complete systems from scratch, such as a URL shortener, a notification service, or a basic chat application. Define your scale assumptions before drawing a single component.

Week 3: Behavioural preparation

Write out several stories from your past using STAR format. Cover these scenarios: a major technical challenge you solved, a conflict you navigated, a failure you recovered from, a time you led or initiated something, and a time you improved a process. Practice saying each story out loud. Speaking reveals gaps that writing hides.

Week 4: Mock interviews and company research

Complete at least a couple of mock interviews with a peer or mentor. Research Kayhut's products and any tech stack details mentioned in their job descriptions. Prepare a few thoughtful questions for your interviewer focused on team structure, engineering practices, deployment culture, and what success looks like in the specific role you applied for.

Jumping to code before thinking: Many candidates start typing before they fully understand the problem. Spend a few minutes restating the problem, clarifying edge cases, and outlining your approach out loud. Interviewers typically reward structured thinking over fast but incomplete solutions.

Generic STAR answers: Saying 'we built a feature and it went well' tells the interviewer nothing useful. Be specific: name the technology involved, describe what made it genuinely hard, and state a concrete outcome.

Treating system design as a quiz with one correct answer: There is no single right design for any system. The mistake is picking an approach without explaining why. Always name the tradeoff you are making and what you are giving up by not choosing the alternative.

Not asking questions at the end: Candidates who ask nothing signal low interest in the role. Prepare questions that show you have thought seriously about the work, not just the interview itself.

Walking in without knowing your salary range: Know the market before salary comes up. Software Engineer salaries in India range from 6-12 LPA at entry level to 40-65 LPA at lead/staff level, per knok jobradar data. Know where your experience places you and be ready to state a number with confidence.