abhibus Software Engineer Interview: Questions & Prep (2026)

AbhiBus is one of India's busiest bus ticketing platforms, connecting millions of travellers to bus operators across the country. The engineering team builds systems for real-time seat inventory, booking flows, payment processing, and partner integrations at scale. With 15 Software Engineer openings listed as of July 2026, the company is actively growing its tech workforce.

Candidates typically go through two to four rounds: an online coding assessment or take-home problem, one or two technical interviews covering algorithms, system design, and backend fundamentals, and a final HR or hiring-manager discussion. The exact number of rounds can vary by team and seniority level, so treat this as a general outline rather than a guaranteed structure.

1. How would you design a real-time seat availability system for bus bookings?
This is the classic AbhiBus system design question. Think about inventory locking, concurrency handling, and keeping availability consistent across operators.

2. Two users try to book the same seat at the same moment. How do you handle this race condition?
Interviewers want to hear about optimistic locking, pessimistic locking, or distributed locks, along with the trade-offs each approach carries.

3. Walk me through how you would design the payment flow for a booking platform. What happens if the payment gateway times out?
Idempotency keys, retry logic, and handling partial failures are central to a strong answer here.

4. How does your system handle a sudden traffic spike, such as during a long weekend when seat demand is very high?
Expect to discuss caching (Redis), load balancing, rate limiting, and queue-based processing for async work.

5. Design a notification system that sends booking confirmations via SMS, email, and push. How do you make it reliable?
This tests async processing, message queues (Kafka or RabbitMQ), and retry strategies on failure.

6. Explain the difference between horizontal and vertical scaling. Which would you choose for the seat inventory service and why?
Interviewers check whether you reason through trade-offs rather than just recite textbook definitions.

7. You have a slow SQL query on a bookings table with tens of millions of rows. How do you diagnose and fix it?
Indexing strategy, EXPLAIN output, query restructuring, and read replicas are all fair game.

8. How would you implement an API rate limiter for the partner operator API?
Token bucket vs leaky bucket, Redis-based counters, and distributed rate limiting come up frequently.

9. Describe a time you debugged a production issue under pressure. What was your process?
This is a behavioural question. Use the STAR format and focus on structured thinking over luck.

10. What is the difference between a process and a thread? How does this relate to concurrency in Java?
AbhiBus's backend is largely JVM-based, so Java concurrency questions are commonly reported by candidates.

11. How would you structure the microservices for a bus booking platform? What are the risks of going micro too early?
Interviewers want you to think about service boundaries, data ownership, and operational complexity.

12. How do you ensure data consistency when a booking spans multiple services (inventory, payment, notification)?
The Saga pattern, two-phase commit, and the outbox pattern are all relevant here.

Q: Two users try to book the same seat simultaneously. How do you prevent double-booking?

*Situation:* In a previous role I worked on a ticketing system for an events venue. Load testing revealed that under concurrent requests, two users could both see a seat as 'available' and complete payment, resulting in double-bookings that required manual refunds.

*Task:* I needed to redesign the seat reservation logic so that simultaneous requests for the same seat could never both succeed.

*Action:* I introduced a short-lived reservation lock using Redis with a TTL of ten minutes. When a user selected a seat, the system attempted to SET the seat key in Redis with NX (set if not exists). Only the first request would succeed; the second received a 'seat taken' response immediately, before touching the database. A background job released locks for abandoned carts. For the final database write at payment confirmation, I added an optimistic lock using a version column, so any race condition that slipped past Redis would still fail safely.

*Result:* Double-bookings dropped to zero in post-launch monitoring. The Redis lock added minimal latency overhead and kept us well within our SLA. The pattern is directly applicable to AbhiBus, where seat inventory faces the same concurrency challenge at a larger scale.

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Q: Describe a time you improved the performance of a slow database query.

*Situation:* Our bookings report page was timing out for operations staff pulling the previous day's summary. The query joined three tables and was doing a full table scan on a bookings table with several million rows.

*Task:* I needed to get the page to load reliably without a full schema migration, since we had a release freeze in place.

*Action:* I ran EXPLAIN ANALYZE and found the query was ignoring an existing index because of an implicit type cast in the WHERE clause, comparing an integer column to a string literal. I fixed the type mismatch, added a composite index on (created_at, status), and rewrote a subquery as a JOIN so the planner could use the index effectively. I also added a short cache with a five-minute TTL for the summary numbers, since exact real-time counts were not required.

*Result:* Query time dropped dramatically and the operations team could access the report reliably each morning. The fix required no schema changes and was safely deployable during the freeze.

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Q: Tell me about a time you had to work across teams to ship a feature.

*Situation:* We needed to add a 'cancel and refund' feature that touched the booking service (my team), the payments service (a separate team), and the operator notification system (a third team). Each team had its own sprint cycle.

*Task:* I was the lead engineer on the booking service side, but the feature would fail if any of the three services shipped incomplete work.

*Action:* I proposed a contract-first approach: we agreed on API schemas and event payloads before anyone wrote code. I drafted a shared document covering error codes, retry contracts, and rollback conditions, and held a short alignment call with all three teams. I also built a stub of the payments service response so my team could develop and test independently. We used feature flags so each service could deploy its changes safely before we turned the feature on end-to-end.

*Result:* The feature launched on schedule with no cross-team blocking issues. Post-launch, refund processing worked correctly for all tested scenarios. The contract-first document became a template other teams adopted for future cross-service work.

STAR for behavioural questions: Every 'tell me about a time' question deserves a clear Situation, Task, Action, Result structure. Keep Situation and Task brief (two to three sentences combined) and spend most of your time on Action and Result. Interviewers at product companies like AbhiBus care more about what you did and what changed than about extended context-setting.

Think-aloud for system design: Do not go silent and produce a finished diagram. State your assumptions out loud, ask one or two clarifying questions (expected scale, consistency requirements, read-to-write ratio), and build the design in layers: start with the core data model, add the API layer, then introduce caching, queuing, and failure handling. This approach shows structured thinking, which interviewers consistently rate highly.

Trade-off framing for technical questions: Whenever you name a technology or pattern, pair it with a limitation. For example: 'Redis gives us fast distributed locks, but if the Redis node goes down mid-booking we need a fallback path.' This signals engineering maturity rather than textbook recall.

Admit uncertainty cleanly: If you do not know something, say so briefly, then reason from what you do know. Candidates who bluff lose credibility fast; candidates who reason carefully under uncertainty often impress.

AbhiBus engineers build systems where a failure at the wrong moment means a traveller misses their bus or gets double-charged. Interviewers are looking for a few qualities beyond raw coding ability.

Reliability thinking. Can you spot the failure modes in a design before they are pointed out? Do you naturally ask 'what happens if this service call fails?' Engineers who think defensively are valued in this domain.

Practical system design. Candidates do not need to know AbhiBus's internal architecture, but they should be able to reason about booking flows, seat locks, and payment idempotency from first principles. Interviewers are not looking for a perfect answer. They are looking for structured thinking.

Clean, working code. For coding rounds, candidates report that correctness matters more than elegance. Write code that runs, handle edge cases explicitly, and then discuss optimisations. Do not optimise prematurely before a working solution exists.

Clear communication. AbhiBus teams are cross-functional. Interviewers want to see that you can explain a technical decision clearly and that you listen to constraints before jumping to solutions.

Ownership mindset. In behavioural rounds, interviewers respond well to candidates who describe problems they personally drove to resolution, not problems their team solved while they observed.

Week 1: Core fundamentals
Revisit data structures (trees, graphs, heaps, hash maps) and practise medium-difficulty problems on arrays, strings, and sliding windows. Focus on getting to a working solution first, then optimise. Pay particular attention to concurrency problems, since AbhiBus's domain makes them likely interview topics.

Week 2: System design
Study these topics in depth: distributed locks and their failure modes, idempotent payment APIs, event-driven architectures using message queues, caching strategies (write-through, write-around, write-back), and database indexing. Try designing a simplified bus booking system from scratch on paper, then stress-test it by asking 'what if this component fails?'

Week 3: Domain and behavioural prep
Book a bus ticket on AbhiBus and pay close attention to the experience: how does seat selection work, what happens when you hold a seat, how does the confirmation flow? This gives you genuine context for design questions. Prepare three to five STAR stories covering: a technically complex problem you solved, a production incident you handled, a cross-team collaboration, and a time you pushed back on a poor technical decision.

Before each round
Candidates report that interviewers appreciate thoughtful questions at the end of the session. Prepare questions about the team's current engineering challenges, their approach to system reliability, or how they handle peak traffic periods.

While you prepare, knok checks 150+ job sites nightly, applies to roles that match your resume, and messages HR on your behalf, so you do not miss AbhiBus or similar openings while your focus is on interview prep.

Jumping into code without clarifying requirements. Interviewers often leave the problem statement deliberately vague. Candidates who start coding immediately miss edge cases that a single clarifying question would have surfaced.

Designing systems without mentioning failure. A design that only describes the happy path signals inexperience. For any component you add, mention at least one failure scenario and how you would handle it.

Vague STAR answers. 'We improved performance significantly' is not a result. 'Query time dropped and unblocked the operations team's daily reporting' is a result. Be specific about what changed and why it mattered.

Treating locking as an afterthought. For a seat booking platform, concurrency is central, not an edge case. Candidates who address locking only when prompted tend to score lower on system design rounds.

Not knowing the basics of your chosen language. If you write Java, expect questions on thread safety, the collections framework, and garbage collection basics. If you write Python, expect questions on the GIL and async patterns. Language fundamentals are checked at product companies.

Over-engineering the design. Proposing a Kubernetes-orchestrated, multi-region, event-sourced architecture for a feature that needs a simple queue and a retry mechanism signals poor judgment. Scale your design to the scale described in the problem, not the theoretical maximum.