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.

Experience
8-12 yrs