- Real-World Systems

The Problem Space :

- Bus is the most complex aggregation problem in Indian travel.

- Thousands of operators.

- Dozens of aggregators.

- Inconsistent APIs.

- Unreliable data contracts.

- Buses don't have NDCs.

- They don't have GDSs.

They have a fragmented ecosystem of state RTCs, private operators, aggregators, and direct integrations - each with their own data models, booking flows, and failure modes.

- 5,000+ operators with wildly inconsistent API standards and reliability profiles

- Real-time seat maps that go stale within seconds

- Boarding point / dropping point logic spanning thousands of city pairs

- Booking confirmation flows with no standard error protocol - just ambiguity

- The engineering challenge here is not glamorous - it is gritty, high-impact, and genuinely hard.

- Building reliability on top of unreliable systems is a craft.

- This is where that craft is practised at national scale.

What You Will Own :

Aggregation & Integration Layer :

- Multi-operator, multi-aggregator integration with standardized abstraction models

- Normalization of inconsistent data contracts - seat types, boarding points, fare classes

- Reliability engineering : circuit breakers, retries, fallbacks, and health-scoring for operators

Search & Availability :

- High-speed route-pair availability search across thousands of operator-route combinations

- Seat map rendering - real-time availability with graceful staleness handling

- Dynamic boarding/dropping point resolution across complex route geographies

Booking & Reconciliation :

- Booking confirmation pipelines across unreliable operators - with idempotency and retry logic

- Financial reconciliation between operator confirmation and payment gateway settlement

- Cancellation flows navigating operator-specific policy variations

AI-First Reliability Engineering :

- LLM-powered operator anomaly detection - spotting unreliable patterns before customer impact

- Intelligent fallback routing - auto-switching operators based on real-time reliability scores

- Agentic reconciliation bots for booking confirmation exceptions

- AI-driven route intelligence and demand forecasting for inventory optimization

Core Engineering Challenges :

- Reliability on Chaos - building SLA guarantees on top of operators with no SLAs

- Data Normalization at Scale - 5,000+ operators, each a different data model

- Booking Ambiguity - operators that confirm, then cancel; or never confirm at all

- Real-Time Freshness - seat availability windows measured in seconds, not minutes

Key Responsibilities :

- Architect and own the end-to-end Bus platform - aggregation, search, booking, and reconciliation

- Build the industry's best operator reliability framework - health scoring, SLA monitoring, automated fallbacks

- Lead the normalization layer that makes chaos manageable for downstream systems

- Partner with Supply and Operations teams to prioritize operator integrations by commercial impact

- Build and lead engineering teams of 1525+ engineers

Technology Stack :

- Backend : Java/Kotlin/Go

- Architecture : Microservices/Kafka/Event-Driven

- Data : Redis/MySQL/DynamoDB

- Cloud : AWS (EKS, S3)

- Observability : Prometheus/Grafana/Alerting Systems

Who You Are :

- 10 - 16 years in backend engineering, with deep experience in aggregation or integration platforms

- Built resilient systems on top of unreliable third-party APIs - you know what circuit breakers feel like at 3am

- Strong instinct for operational excellence - not just building systems, but keeping them alive

- Tier-I institute background strongly preferred (IIT / IIIT / NIT / IISC / BITS - CSE/ISE)

Why This Matters :

- Bus is the mobility backbone of Bharat - this platform moves millions of people daily

- Reliability engineering here is a genuine craft problem, not a checklist exercise

- Build the most reliable bus aggregation system in the world, from scratch