Senior Backend Engineer

HackerRank runs the technical assessment platform used by companies including NVIDIA, Amazon and Microsoft, and says it is trusted by over 2,500 organisations, so the backend has to stay correct under exam conditions where a failure is immediately visible to a candidate mid interview. This is production backend engineering in that environment, and the posting describes a broad remit: architecting and leading complex backend systems, setting technical strategy for your domain, owning end to end reliability and performance including SLOs and incident response, and designing API frameworks and data models that other product teams build on. It also covers leading cross functional initiatives across frontend, infrastructure, product and design, driving large refactors of legacy systems, and mentoring. One section deserves attention before you apply: HackerRank makes AI fluency a first class requirement here rather than a nice to have, and the expectations are specific enough that most candidates will want to prepare for that part deliberately.

What the posting requires

- 3 to 6 years building and operating production backend systems at scale
- Expert level in at least one modern backend language such as Python, Ruby, Go, Java or Node.js, with strong fundamentals across the stack
- Proven ability to design distributed systems, having made real architectural decisions on service decomposition, data consistency, fault tolerance and observability
- Deep expertise with relational databases such as PostgreSQL and MySQL plus NoSQL stores, including schema design, query optimisation and modelling for high throughput
- Strong understanding of caching with Redis or Memcached, asynchronous messaging with Kafka or RabbitMQ, and event driven architecture
- Hands on containerisation with Docker and Kubernetes, CI/CD pipelines and infrastructure as code
- A track record of taking technical projects from an ambiguous problem statement through to production

Read the AI section before you apply

Unusually, HackerRank lists AI fluency as its own requirement block rather than a bonus, and asks for:

- Deep hands on proficiency with AI powered development tools such as GitHub Copilot, Cursor or Claude Code, to the point where you have built workflows and practices you could teach to others
- Strong working knowledge of large language models and agentic systems, including capabilities, limitations, context management and tool use
- Proven ability to use AI across the full development lifecycle: architecture exploration, implementation, code review, test generation, documentation, incident analysis and technical writing
- Solid understanding of AI and ML fundamentals: transformer architectures, embedding models, inference optimisation, retrieval augmented generation, the tradeoff between fine tuning and prompt engineering, and evaluation methods
- The ability to evaluate AI tooling and integration patterns on cost, latency, reliability and security

Counts as a bonus, not a requirement

- Systems serving millions of concurrent users under strict latency and availability requirements
- Deep expertise in patterns such as microservices, CQRS, event sourcing or domain driven design
- Significant cloud experience across AWS, GCP or Azure including serverless and cost optimisation
- Building platform level APIs, SDKs or developer tools used by other engineering teams
- A history of improving engineering culture, through RFC processes, architecture review boards or engineering blog writing

Location and working pattern

Hybrid in Bangalore, so plan for regular office days.

A good fit if

You have a few years of backend work and want a product where reliability genuinely matters, and you are already comfortable working with AI tooling rather than avoiding it.

Think twice if

You have kept AI tools at arm's length. That section is long, specific and clearly being screened on, and it is the part of this posting most likely to decide the outcome.