Lead DevOps Engineer

20 hours ago

Anywhere in IndiaMultiple Locations Scaling Theory Technologies Pvt Ltd Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Description :


As the Lead DevOps Engineer, you'll own our cloud infrastructure and CI/CD ecosystem end-to-end. You'll design scalable, secure, and automated cloud systems, mentor engineers, and collaborate closely with backend, data, and product teams to deliver resilient, high-performance environments.

Responsibilities :


- Design, implement, and maintain GCP-based cloud infrastructure (Compute Engine, Cloud Run, GKE, Pub/Sub, Cloud SQL, BigQuery, etc.).

- Build and manage CI/CD pipelines using tools like GitHub Actions, Jenkins, or Cloud Build.

- Automate infrastructure provisioning via Terraform / Helm / Ansible.

- Monitor, optimize, and secure cloud performance, cost, and availability.

- Implement DevSecOps best practices, including IAM policies, secret management, and vulnerability scanning.

- Set up observability stacks (Prometheus, Grafana, ELK, Stackdriver).

- Collaborate with engineering teams to design scalable, fault-tolerant architectures.

- Own incident management and on-call rotations, driving root-cause analysis and uptime improvement.

- Mentor and lead junior DevOps/SRE engineers, setting standards for automation and operational excellence.

Requirements :


- 6+ years of DevOps/SRE experience with 2+ years leading small teams or projects.

- Hands-on expertise with GCP services and Terraform / Infrastructure as Code (IaC).

- Strong command of Linux systems, Docker/Kubernetes, and CI/CD pipelines.

- Proficiency in scripting with Python / Bash / Go.

- Solid understanding of networking, security, and cloud cost optimization.

- Familiarity with monitoring, alerting, and logging tools (Grafana, Datadog, Stackdriver).

- Experience in fast-paced startup environments or scaling systems from MVP to production.

Nice-to-Have :


- GCP certification (Professional DevOps Engineer / Cloud Architect).

- Experience with multi-cloud or hybrid environments.

- Exposure to microservices, service mesh (Istio), or GitOps (ArgoCD, Flux).

- Background in data engineering or ML infrastructure is a plus.

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