DevJobs

DevOps Engineer

Overview
Skills
  • SQL SQL
  • Shell Shell
  • Python Python
  • PowerShell PowerShell
  • Linux Linux
  • CI/CD CI/CD
  • AWS AWS
  • Kubernetes Kubernetes ꞏ 3y
  • Ansible Ansible
  • Terraform Terraform
  • EKS ꞏ 3y
  • Lambda
  • VPC
  • S3
  • RDS
  • Network infrastructure
  • Firewalls
  • DNS
  • CloudWatch
  • CDN tools
  • AI agents
  • AI tools

We are looking for a Senior DevOps Engineer to join our DevOps team. In this role, you will take ownership of the production environment, lead strategic operational processes, and partner closely with R&D teams to drive reliability, scalability, and automation across the organization. You will be responsible for designing, building, and evolving our infrastructure and automation solutions, with a strong emphasis on modern DevOps best practices.


Responsibilities

  • Own and maintain a mature, stable, and secure production environment
  • Collaborate with cross-functional engineering teams to support development and delivery processes
  • Design and implement best-practice CI/CD solutions, especially for microservices architectures
  • Maintain, upgrade, and take end-to-end ownership of Kubernetes clusters (EKS)
  • Build, manage, and optimize the company’s cloud infrastructure




Requirements:


  • 6+ years of experience as a DevOps engineer (strong infrastructure background required)
  • Deep hands-on experience with AWS services (RDS, VPC, Lambda, S3, CloudWatch, networking, etc.)
  • 3+ years of production experience with Kubernetes, specifically EKS 
  • Strong experience with network infrastructure, Linux, firewalls, DNS, CDN tools
  • Experience with configuration management tools (e.g., Ansible)
  • Proven ability to create and maintain CI/CD pipelines
  • Experience with Infrastructure as Code (preferably Terraform)
  • Database knowledge: SQL, RDS
  • Coding/scripting skills: Python, PowerShell, Shell

Advantages

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


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