We are looking for an AI-first DevOps Engineer with 3+ years of hands-on experience in cloud infrastructure, containerization, CI/CD automation, Infrastructure as Code (IaC), and observability.
The ideal candidate combines strong DevOps fundamentals with an AI-first mindset, leveraging AI tools, AI agents, and intelligent automation to improve engineering productivity, streamline operations, enhance reliability, and solve complex infrastructure challenges.
We value engineers who go beyond traditional approaches, continuously explore emerging AI capabilities, and use AI as a core part of their daily engineering workflow.
Key Responsibilities
Cloud Infrastructure & DevOps Engineering
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.
Our expectation: We are looking for engineers who are not just capable of managing infrastructure, but who can rethink how DevOps is performed in an AI-first engineering environment.