Lead DevOps Engineer

The core responsibilities for the job include the following:

Cloud and Infrastructure:

• Design, build, and maintain cloud infrastructure primarily on AWS (mandatory) and GCP (secondary).
• Manage compute, networking, storage, IAM, and managed services for production workloads.
• Implement Infrastructure as Code (IaC) using tools like Terraform / CloudFormation.

CI/CD and Automation:

• Build and maintain CI/CD pipelines for backend services, AI workflows, and internal tools.
• Automate deployments, rollbacks, environment provisioning, and operational workflows.
• Write Python scripts for automation, tooling, and operational tasks.

Reliability and Observability:

• Ensure high availability, scalability, and performance of production systems.
• Set up monitoring, logging, and alerting (e. g., CloudWatch, Prometheus, Grafana).
• Drive incident response, root cause analysis, and post-mortems.

Security and Compliance:

• Implement and enforce cloud security best practices (IAM, secrets management, and network security).
• Work on vulnerability management, secure CI/CD, and least-privilege access.
• Support compliance and security reviews for customer and partner integrations.

Collaboration:

• Partner with backend, AI/ML, and product teams to enable smooth releases.
• Contribute to platform architecture decisions and technical standards.

Requirements:

• Mandate: Strong hands-on experience in DevOps / Platform Engineering, AWS, Kubernetes, and Terraform/IaC, with media industry exposure preferred.
• 6-10 years of hands-on DevOps / Platform Engineering experience.
• Strong experience with AWS (EC2, EKS/ECS, S3, RDS, IAM, VPC, CloudWatch, etc. )
• Working knowledge of GCP (Compute, GKE, networking, IAM).
• Strong Python scripting skills with an automation-first mindset.
• Solid understanding of cloud security principles and best practices.
• Experience with Linux, containers (Docker), and container orchestration (Kubernetes).

Good to Have:

• Experience with Terraform or similar IaC tools.
• Exposure to multi-cloud or hybrid cloud setups.
• Experience supporting AI/ML or data-heavy workloads.
• Familiarity with secrets management tools (Vault, AWS Secrets Manager).
• Knowledge of cost optimization and cloud FinOps practices.