DevOps Engineer

Location: Columbus, OH Salary: $65.00 USD Hourly - $70.00 USD Hourly Description:
Dev Ops Engineer (Git Ops / Kubernetes / AWS)

Location: Columbus, OH (Onsite)

Job Type: Contract-to-Hire

About the Role

Our client is seeking a highly skilled Dev Ops Engineer to design, build, and maintain cloud-native infrastructure and automation solutions. This role requires strong expertise in Kubernetes, Git Ops, Terraform, and AWS
, along with experience developing automation tools using Python.

You will play a critical role in enabling scalable, reliable, and secure systems, including support for modern ML platforms and pipelines
.

Key Responsibilities

• Design, develop, and maintain cloud-native infrastructure using Kubernetes, Docker, and AWS services (EKS, ECS, ECR)
• Implement and manage Git Ops workflows for continuous deployment and environment consistency
• Build and maintain Infrastructure as Code (IaC) using Terraform
• Develop Python-based automation tools to improve operational efficiency and system reliability
• Design and support CI/CD pipelines using tools such as Jenkins and Git Lab CI
• Deploy and manage applications in Kubernetes environments using Helm
• Implement monitoring and observability using Prometheus and Grafana
• Identify and automate remediation of recurring operational issues
• Collaborate with cross-functional teams to review architecture, evaluate tools, and drive technical decisions
• Contribute to the design and development of scalable ML platforms
  , including training, deployment, and monitoring pipelines

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field (required)
• Hands-on experience with Kubernetes and Docker in production environments
• Strong knowledge of Git Ops practices (e.g., ArgoCD, Flux)
• Expertise in Terraform (Infrastructure as Code)
• Experience with AWS services
  , especially EKS, ECS, and ECR
• Proficiency in Python for automation and tooling
• Experience building and managing CI/CD pipelines
• Strong understanding of cloud-native architecture and deployment strategies
• Solid debugging, troubleshooting, and system design skills

Preferred Qualifications (Nice to Have)

• Experience with MLOps or AI/ML platforms (Kubeflow, model pipelines)
• Knowledge of deployment strategies (Blue/Green, Canary, Rolling)
• Familiarity with multi-cloud environments (AWS, Azure, Google Cloud Platform)
• Experience with advanced monitoring and observability practices
• Exposure to agentic/AI-driven automation systems
  

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