About the Role
We are seeking an experienced Senior DevOps Engineer to design, implement, and maintain scalable, secure, and reliable infrastructure and deployment pipelines. The ideal candidate will have strong expertise in cloud infrastructure, CI/CD automation, containerization, infrastructure as code, monitoring, and system reliability. The candidate will work closely with development, QA, security, and infrastructure teams to streamline software delivery, improve operational efficiency, and ensure high availability and performance across environments. Familiarity with AI/ML workloads or AI-enabled applications will be an added advantage.
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Key Responsibilities
- Design, implement, and maintain CI/CD pipelines to automate application build, testing, deployment, and release processes.
- Manage and optimize cloud infrastructure on platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
- Implement Infrastructure as Code (IaC) using Terraform, Ansible, or similar tools.
- Deploy, manage, and troubleshoot containerized applications using Docker and Kubernetes.
- Develop and maintain automation scripts using Python, Bash, PowerShell, or other scripting languages.
- Monitor infrastructure, applications, and services to ensure availability, performance, scalability, and reliability.
- Implement logging, monitoring, alerting, and incident management practices using tools such as Prometheus, Grafana, ELK Stack, or equivalent solutions.
- Apply DevSecOps practices by integrating security checks, vulnerability scanning, secrets management, and compliance controls into CI/CD pipelines.
- Manage cloud networking, access controls, IAM, backup, disaster recovery, and infrastructure security.
- Troubleshoot complex infrastructure and deployment issues, perform root-cause analysis, and implement preventive measures.
- Collaborate with software engineering teams to improve deployment strategies, release management, and development workflows.
- Establish and maintain operational documentation, infrastructure standards, and best practices.
- Identify opportunities to optimize cloud resource utilization, improve system efficiency, and reduce operational costs.
Required Skills & Experience
- 6–10 years of professional experience in DevOps, cloud infrastructure, systems engineering, or a related field.
- Strong hands-on experience with at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud Platform.
- Proven experience designing and maintaining CI/CD pipelines using tools such as Jenkins, GitLab CI/CD, GitHub Actions, or Azure DevOps.
- Strong knowledge of Docker, Kubernetes, and container orchestration.
- Practical experience with Infrastructure as Code tools such as Terraform, Ansible, or CloudFormation.
- Proficiency in scripting and automation using Python, Bash, or PowerShell.
- Experience with Linux administration and troubleshooting; familiarity with Windows Server is an advantage.
- Knowledge of networking fundamentals, DNS, load balancing, firewalls, VPNs, and cloud networking.
- Experience with monitoring, logging, alerting, and performance optimization.
- Understanding of DevSecOps principles, cloud security, IAM, secrets management, and vulnerability management.
- Experience with Git and version control workflows.
- Strong analytical, troubleshooting, communication, and collaboration skills.
Nice to Have
- Relevant certifications such as AWS Certified DevOps Engineer, Microsoft Certified: DevOps Engineer Expert, Google Cloud Professional Cloud DevOps Engineer, or Kubernetes certifications.
- Experience managing production systems with high availability, scalability, and disaster recovery requirements.
- Familiarity with GitOps practices and tools such as Argo CD or Flux.
- Experience with cloud cost optimization and performance tuning.
- Knowledge of serverless architectures and automated testing strategies.
AI/ML Experience
- Familiarity with deploying or supporting AI/ML-enabled applications in cloud environments.
- Basic understanding of the infrastructure requirements for applications using Large Language Models (LLMs) or Generative AI.
- Exposure to deploying AI services, model APIs, or inference workloads through containerized environments.
- Experience supporting monitoring, scalability, security, and CI/CD automation for AI-enabled applications.
- AI/ML experience is an added advantage and is not a core requirement for this role.
Work Location and Employment Details
- Location: Abu Dhabi, UAE
- Employment Type: Full-Time, Permanent
- Work Mode: Onsite
- Required Experience: 6–10 years
- Monthly Salary: AED 25,000
Why Join Us?
- Opportunity to work on modern cloud infrastructure and enterprise-scale platforms.
- Collaborate with cross-functional engineering teams.
- Contribute to automation, infrastructure reliability, and continuous improvement initiatives.
- Work in an environment that values engineering excellence, security, and operational efficiency.
