About the role
- Design, deploy, and manage scalable, secure, and cost-efficient cloud infrastructure across AWS, Azure, and GCP.
- Build, maintain, and optimize CI/CD pipelines using GitHub Actions and GitLab CI/CD.
- Containerize applications using Docker and orchestrate workloads using Kubernetes.
- Define, provision, and maintain Infrastructure as Code using Terraform.
- Automate configuration management and operational tasks using Ansible.
- Implement monitoring, logging, and observability solutions using Prometheus, Grafana, ELK Stack, and related tools.
- Troubleshoot complex infrastructure, deployment, networking, and application issues across environments.
- Continuously improve infrastructure reliability, security, scalability, and cloud cost efficiency.
Required Skills & Experience
- 3+ years of experience in DevOps, Cloud Engineering, or Platform Engineering.
- Strong hands-on experience with one or more major cloud platforms: AWS, Azure, or GCP.
- Practical experience with Docker and Kubernetes.
- Experience with Terraform and Infrastructure as Code principles.
- Experience building and maintaining CI/CD pipelines using GitHub Actions, GitLab CI/CD, or similar tools.
- Working knowledge of Ansible or other configuration management and automation tools.
- Experience with monitoring, logging, and observability tools such as Prometheus, Grafana, and ELK Stack.
- Scripting and automation skills using Python, Bash, or similar languages.
- Familiarity with AI coding assistants, LLM-based tools, or AI-powered engineering workflows.
- Ability to identify repetitive tasks and design automation-driven solutions.
- Strong troubleshooting, analytical thinking, and problem-solving skills.
- A continuous learning mindset and willingness to experiment with emerging technologies.
- AWS Certified Solutions Architect Associate / Professional (mandatory)
- Additional cloud or Kubernetes certifications are a plus, such as Azure Solutions Architect Expert, Google Professional Cloud Architect, or CKA/CKAD/CKS
What We Expect from an AI-First Engineer
- AI by default: Evaluate whether AI can improve the speed, quality, or effectiveness of a task.
- Automation mindset: Eliminate repetitive manual work wherever practical.
- Critical thinking: Verify AI-generated solutions instead of accepting outputs blindly.
- Ownership: Take responsibility for the reliability, security, and quality of delivered solutions.
- Continuous experimentation: Explore new AI capabilities and turn useful experiments into practical engineering improvements.
- Knowledge sharing: Help teammates adopt effective AI-assisted engineering practices.
- Measurable productivity improvements from AI-enabled engineering practices.