Senior Backend Engineer - Systems, Database & Performance

Location : Gurgaon

Experience : 6 - 12+ years

About the role :

Animall builds technology products for Bharat, serving millions of users across cattle commerce, agriculture, livestock services, subscriptions, and other digital products.

We are looking for a deeply hands-on backend engineer who can own complex production systems and make them simpler, faster, more reliable, and easier to scale.

You'll often be given problems rather than detailed specifications. We expect you to independently investigate, design, implement, deploy, measure, and iterate.

What you'll own :

- Backend architecture and service design

- Database architecture, schema design, and query performance

- High-scale APIs and asynchronous systems

- Performance bottlenecks across application, database, cache, and infrastructure layers

- Reliability, observability, and production debugging

- Background jobs, queues, retries, idempotency, and concurrency

- Large-data migrations and zero/minimal-downtime schema changes

- Capacity planning and scaling systems as traffic and data grow

- Simplifying existing systems and reducing technical debt

- Improving engineering practices across the backend team

What we're looking for :

You should have strong practical experience with most of the following :

Backend & distributed systems :

- Strong experience designing and operating production systems at meaningful scale, including APIs, distributed systems, asynchronous processing, queues, caching, concurrency, failure handling, rate limiting, backpressure, and pragmatic service design.

Deep database expertise :

- Strong PostgreSQL and/or MySQL fundamentals including schema design, indexing, query optimization and execution plans, transactions, isolation levels, locks/deadlocks, connection pooling, replication, partitioning, migrations, pagination at scale, and data consistency. We want someone who understands what the database is doing underneath the ORM.

Performance & production engineering :

- You should be comfortable systematically debugging slow APIs, high DB load, memory/CPU issues, connection exhaustion, query regressions, p95/p99 latency, traffic spikes, hot rows/keys, slow background jobs, and infrastructure bottlenecks. We value engineers who use metrics, traces, logs, profiling, query plans, and experiments rather than guessing - and who have personally owned difficult production incidents end to end.

AI-native engineering :

- You should actively use modern AI coding and reasoning tools to improve both speed and quality across codebase exploration, implementation, refactoring, testing, debugging, code review, database analysis, migrations, documentation, and architecture. At the same time, you must have the technical judgment to challenge AI output, detect bad assumptions, validate correctness and security, and verify changes through tests, benchmarks, metrics, and production data. We are looking for engineers who get significant leverage from AI - not engineers who blindly accept generated code.

Engineering philosophy :

You will likely do well here if you believe :

- Simple systems are usually better than clever systems.

- Measure before optimizing.

- Production behaviour matters more than architecture diagrams.

- Database knowledge is a superpower.

- Reliability is part of product quality.

- Good engineers reduce complexity.

- Senior engineers should own outcomes, not just tickets.

- AI should remove low-value engineering work and increase the amount of high-quality work one engineer can deliver.

What we don't care much about :

We do not require :

- Experience with one exact programming language

- Memorising framework APIs

- Competitive programming expertise

- LeetCode-style algorithm preparation

- A specific number of years at famous companies

What would make you stand out :

Any of these are strong signals :

- You have operated databases with hundreds of millions or billions of rows.

- You have significantly reduced production latency or infrastructure cost.

- You have diagnosed difficult concurrency or consistency bugs.

- You have executed large production migrations safely.

- You have built systems processing large volumes of asynchronous jobs/events.

- You have materially improved reliability of a production system.

- You can explain a major production incident you personally debugged end to end.

- AI tools have changed the way you engineer, and you can demonstrate how you use them.

What you'll get :

- High ownership

- Direct access to engineering and company leadership

- Real production systems and meaningful scale

- Fast decision-making

- Minimal process overhead

- Opportunity to shape architecture and engineering standards

- Ability to ship meaningful changes quickly

- A team that actively embraces AI-powered engineering