Responsibilities:
- Design, develop, and maintain backend services using Golang, ensuring high code quality, performance, and maintainability.
- Participate in system design and architectural discussions, including service boundaries, data flow, scalability, and reliability trade-offs.
- Build, deploy, and operate applications on Google Kubernetes Engine (GKE):
- Package and deploy applications using Helm charts
- Configure resources, autoscaling, health checks, and rollout/rollback strategies
- Troubleshoot production issues related to performance, stability, networking, and resource usage
- Manage cloud infrastructure (GCP) using Terraform (Infrastructure as Code):
- Create, maintain, and review Terraform modules
- Ensure consistent and reliable environments across development, staging, and production
- Improve system reliability, observability, and security:
- Implement and use logging, metrics, tracing, and alerting
- Participate in incident response, root cause analysis, and post-incident improvements
- Collaborate closely with product, DevOps, and engineering teams to deliver secure, production-ready solutions.
Requirements & Qualifications:
- 6+ years of experience in backend or platform engineering in production environments.
- Solid understanding of distributed systems concepts such as scalability, reliability, retries, timeouts, and consistency.
- Strong hands-on experience with GKE / Kubernetes, including:
- Core Kubernetes resources (Deployments, Services, Ingress, ConfigMaps, Secrets)
- Deploying and managing applications using Helm charts
- Debugging and operating production workloads
- Strong understanding of GCP core services, including IAM, VPC, Subnets, Cloud NAT, VPN, Load Balancing, Cloud DNS, Cloud Logging, Cloud Run and Monitoring.
- Practical experience with Terraform for infrastructure provisioning and management.
- Experience with CI/CD pipelines and cloud-native application operations.
- Strong proficiency in Golang, including:
- Concurrency (goroutines, channels), context handling, and error management
- Building and maintaining APIs (REST and/or gRPC)
- Writing clean, testable, and maintainable code
- Strong problem-solving skills and the ability to work with complex systems.
- Good communication skills and a strong sense of ownership.
Optional (Nice to Have):
- Basic knowledge of AI systems and GenAI fundamentals, including AI agents, RAG architectures, and LLM-based services.
- Familiarity with AI infrastructure concepts:
- Model inference services
- GPU-based workloads
- Scaling, latency, and cost trade-offs
- Experience with service mesh (e.g., Istio)
- Familiarity with observability tools (Prometheus, Grafana, Cloud Monitoring)
- Good understanding of cloud and application security, including:
- IAM and access control (GCP IAM, Kubernetes RBAC)
- Secrets management and secure configuration
- Secure service-to-service communication (mTLS)
- Container and Kubernetes security best practices