Staff DevOps Engineer

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.