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Senior DevOps Engineer

Personetics·Givatayim, Israel·en
Not specifiedFull-timeDevOps & SREFinTechEnterprise Software

Join an engineering team building and operating a cloud-native financial software platform. Own scalable infrastructure, deployment workflows, and production reliability while supporting cloud transformation and AI services.

Responsibilities

  • Design, implement, and maintain CI/CD pipelines and deployment workflows.
  • Manage and optimize cloud infrastructure across AWS, Azure, or GCP.
  • Build and maintain infrastructure as code using Terraform.
  • Operate Kubernetes environments and GitOps workflows with Argo CD and Kustomize.
  • Support migration of enterprise applications and infrastructure from on-premises environments to SaaS and cloud-native architectures.
  • Maintain production environments for availability, security, scalability, and operational excellence.
  • Own production operations, including incident management, root cause analysis, capacity planning, and performance optimization.
  • Partner with Engineering, QA, Product, and AI teams.
  • Support deployment, operation, and monitoring of AI and generative AI services.
  • Build and maintain monitoring, logging, and alerting systems.
  • Troubleshoot infrastructure, deployment, and production issues.

Requirements

  • At least 5 years of experience in DevOps or a similar infrastructure-focused role.
  • Hands-on experience with AWS, Azure, or GCP.
  • Experience with CI/CD tools such as Jenkins or GitHub Actions.
  • Strong knowledge of Terraform and infrastructure as code.
  • Experience with Docker, Kubernetes, Argo CD, and Kustomize.
  • Experience operating production-grade Kubernetes SaaS platforms or enterprise environments.
  • Experience with monitoring and observability tools such as Prometheus, Grafana, and ELK.
  • Bachelor’s degree in computer science, computer engineering, information systems, or a related field, or equivalent practical experience.

Nice to have

  • Experience supporting AI, machine learning, or generative AI workloads, MLOps concepts, AI deployment platforms, or cloud AI services.

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