Razorpay
Razorpay is India's leading payments infrastructure company, powering payment acceptance for 8M+ businesses.
- Rounds
- 5–6 rounds
- Focus
- Machine Coding + System Design + Leadership
- System Design
- Yes
- Timeline
- 4–5 weeks to offer
Rounds 1–2 are fully visible · Round 3+ unlocked with a free account
Machine Coding
Production-grade LLD where the bar is a codebase another team would be comfortable owning on Day 1. Recent SDE-3 problems include distributed lock managers, reconciliation engines, and in-memory search engines with ranking. At this level, Razorpay expects you to proactively define error contracts ('is this error retryable?'), plan for extensibility ('how do you add a new matching algorithm in 6 months?'), and write a comprehensive test plan without being asked.
What to expect
- e.g., 'Build a Reconciliation Engine' — match payment events against bank settlement files, flag discrepancies, handle partial matches and clock skew between systems
- e.g., 'Implement a Distributed Lock Manager' — Redis-based lease with TTL, heartbeat renewal, fencing tokens to prevent stale writes after lock expiry, quorum-based Redlock for split-brain, leader election
- e.g., 'Design an In-Memory Document Search Engine' — inverted index, TF-IDF ranking, incremental index updates without full rebuild
- Extensibility gate: 'How do you add a new reconciliation rule (e.g., match by bank reference instead of transaction ID) without rewriting the matching core?'
- Error contracts: 'What does your lock acquisition API return when the lock is held? Is it retryable? How does a caller know the difference from a network timeout?'
- Full test plan expected — concurrent lock acquisition, TTL expiry during operation, split-brain on Redis failover
Practice on KnowEase.AI
Coding Round
Hard DSA connected to production payment infrastructure. At SDE-3, Razorpay frames algorithmic problems as optimizations you'd actually make in a payment system — the expectation is that you can translate an algorithmic insight into a system-level argument. Parallelization and memory pressure at scale are expected follow-ups.
What to expect
- e.g., 'Design a data structure for real-time fraud scoring on a stream of payment events — support O(log n) range sum queries for sliding windows' — Binary Indexed Tree or segment tree
- e.g., 'Find all strongly connected components in a merchant→acquirer→bank routing graph — these represent circular dependencies in settlement' — Tarjan's SCC
- e.g., 'Given a large transaction log, find all merchants reachable from a flagged merchant within 3 hops' — BFS with depth bound, efficient adjacency representation
- Parallelization follow-up: 'How would you partition this BFS to run across 10 machines handling 10M transactions/minute?'
- Memory pressure: 'At what graph size does your adjacency matrix become impractical? What's your alternative and its trade-offs?'
Practice on KnowEase.AI
System Design 1
API & SDK Design (LLD)
Leadership Round
About Razorpay SDE-3 Interviews
Razorpay SDE-3 interviews include dual system design rounds, a leadership discussion, and — as of 2024–2025 — an agentic coding round where the candidate drives an AI-assisted implementation session. The design rounds test settlement pipeline architecture, compliance constraints (RBI data residency, PCI-DSS), and SDK versioning for 200K+ merchants. Leadership questions are specific: cross-team alignment on competing priorities, zero-downtime migrations, and where to invest in payments infrastructure for the next three years.
See interview loops for other companies:
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Practice the exact topics tested in each round — DSA, system design, SQL, and LLD.
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