About the role
Responsibilities:
- Design, implement, and manage scalable, highly available cloud infrastructure on GCP and/or AWS.
- Build and maintain CI/CD pipelines using Jenkins, GitLab CI/CD, GitHub Actions, or similar tools.
- Manage containerized environments using Docker and Kubernetes (EKS/GKE preferred).
- Develop Infrastructure as Code (IaC) using Terraform, Ansible, and CloudFormation. Set up observability using tools like Prometheus, Grafana, ELK Stack, or similar.
- Manage and optimize database infrastructure for systems like MongoDB, PostgreSQL, Redis, and Cassandra, including backups, replication, scaling, and monitoring.
- Collaborate closely with backend, iOS, QA, and security teams to streamline and secure software delivery. Implement and enforce DevSecOps best practices across environments and workflows.
- Drive automation, environment standardization, and cost optimization across infra components.
- Own uptime, incident response, rollback planning, and postmortems.
Requirements:
- 10-12 years of hands-on experience in DevOps, SRE, or infrastructure engineering roles.
- Strong command of AWS or GCP services: compute, networking, IAM, monitoring, etc.
- Experience building and scaling CI/CD pipelines for rapid, safe releases.
- Solid knowledge of Kubernetes, Helm, and container orchestration best practices.
- Proficient in scripting and automation using Python, Bash, or Go.
- Experienced in Infrastructure-as-Code (Terraform, Ansible, CloudFormation).
- Solid grasp of networking fundamentals, DNS, firewalls, security groups, load balancers, etc.
- Comfortable with monitoring, logging, and alerting stacks (ELK, Prometheus, Datadog, etc. ).
- Strong debugging, incident resolution, and system design thinking.
- Bias for ownership, hands-on mindset, and ability to thrive in ambiguity.
Bonus (Nice to Have):
- Exposure to startup environments or zero-to-one infra builds.
- Interest in privacy, security, or compliance in consumer apps.
- Familiarity with cost optimization, autoscaling, or spot instance strategies.
- Worked with mobile app backend systems (push infra, image/video processing, etc. ).
- Experience with ML Ops pipelines (e. g., model deployment, versioning, monitoring).
- Understanding of GPU optimization and autoscaling for AI/ML workloads.