Senior Software Engineer
New
IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- 5+ years
- Required Skills
- KafkaKubernetesGoCI/CDTerraformDistributed Systems
Requirements
- 5+ years of software engineering experience, with strong focus on backend systems, distributed systems, platform engineering, or DevOps environments.
- Strong production experience with Golang or similar backend programming languages.
- Proven experience owning complex engineering projects end-to-end in production environments.
- Deep understanding of distributed systems, event-driven architectures, asynchronous processing, and reliability engineering principles.
- Hands-on experience with messaging or streaming systems such as Kafka, NATS, or RabbitMQ.
- Experience deploying and operating services in Kubernetes and containerized environments.
- Familiarity with infrastructure-as-code tools such as Terraform, Helm, or Terragrunt.
- Strong understanding of observability practices including logging, monitoring, alerting, and incident response.
- Experience working in CI/CD-driven environments with a focus on automation and developer productivity.
- Strong AI-native engineering mindset, with practical use of coding assistants, automation tools, and AI workflows to improve productivity and system quality.
- Excellent written and verbal communication skills in English.
Responsibilities
- Own end-to-end development of platform features, including discovery, design, implementation, testing, deployment, and production operations.
- Translate customer and operational challenges into scalable backend and infrastructure solutions, improving provisioning, reliability, observability, and performance.
- Design and build distributed backend systems in Go, ensuring scalability, fault tolerance, and operational efficiency.
- Define and track key platform metrics such as deployment success rate, provisioning time, system reliability, and infrastructure utilization.
- Collaborate with cross-functional teams including DevOps, product, support, and infrastructure to evaluate trade-offs and prioritize platform improvements.
- Identify system bottlenecks and reliability risks, driving long-term architectural and operational improvements from concept to production.
- Apply AI-augmented engineering practices to accelerate development, testing, documentation, and operational analysis while maintaining high correctness standards.
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