We are seeking a highly skilled and experienced DevOps Team Lead to join our dynamic team.

As the DevOps Team Lead, you will be responsible for leading and managing a global team of DevOps engineers, driving our cloud-native infrastructure, and ensuring the reliability, scalability, and security of our production environments.

Key Responsibilities:

• Lead and manage a global team of 5 DevOps engineers, fostering collaboration, mentorship, and professional growth
• Design, implement, and optimize cloud-native architectures using AWS services
• Manage and scale our Kubernetes (K8S) clusters to support our microservices architecture
• Drive the implementation of CI/CD pipelines and infrastructure-as-code practices
• Ensure high availability, performance, and security of our production environments
• Collaborate with development, product, and security teams to align DevOps practices with business goals
• Continuously improve our DevOps processes, tools, and methodologies.

Requirements:

• At least 3+ years of experience leading and managing a global DevOps team
• At least 5+ years of hands-on DevOps experience in production environments
• At least 5+ years of experience with AWS services (EC2, EKS, S3, RDS, SCP etc.)
• Proven experience in High scale production environment
• Expert knowledge of Kubernetes (K8S) and container orchestration, including completion tools
• Strong scripting skills (Python, Bash, etc.) and experience with infrastructure-as-code (Terraform, CloudFormation)
• Experience with CI/CD tools (Jenkins, GitLab CI, etc.) and version control systems (Git)
• Familiarity with monitoring and logging tools (Prometheus, Grafana, ELK stack, APM monitoring)
• AWS certifications (Solutions Architect, DevOps Engineer)
• Experience with serverless architectures (AWS Lambda, API Gateway)
• Knowledge of security best practices and compliance frameworks (ISO 27001, SOC 2)
• Experience with GitOps methodologies and tools (ArgoCD, Helm, admission control)

Experience in any of the following is a strong plus:

• Leveraging AI tools to improve DevOps workflows, observability, and engineering productivity
• Building self-service capabilities and internal developer platforms
• Designing and implementing workflows involving AI agents, including automation, monitoring, and incident response use cases