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Open nowFirst seen 6 days ago

DevOps Engineer

Personetics11 open roles

Where
Tel Aviv, Israel
Work mode
Hybrid
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Your applicationOpen nowDevOps EngineerPersonetics · Tel Aviv, Israel

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on Personetics's own form.

The reply lands in your private mailbox

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The clock on this job

Early applications get read.

12.3% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.7%14 days
  5. 35.6%30 days
  6. 58.0%60 days
This job: first seen 6 days ago

The posting

Personetics is shaping the Cognitive Banking era, harnessing AI to help banks anticipate customer needs, provide actionable insights, and deliver intelligent financial guidance. Our platform continuously analyzes and leverages real-time transactional data, enabling banks to proactively support customers in managing their finances and reaching their goals.

As industry leaders — yes, we really are leaders — we partner with the world’s top financial institutions, empowering over 150 million customers monthly across 35 global markets from offices in New York, London, Singapore, São Paulo, and Tel Aviv.

About the position

We are seeking an experienced DevOps Engineer to join our Engineering team and play a key role in building and operating our cloud-native platform. The ideal candidate will have hands-on experience managing production environments at scale, driving cloud transformation initiatives, and supporting the of enterprise systems from on-premises deployments to modern SaaS and cloud-native architectures. You will be responsible for designing, automating, and maintaining scalable infrastructure, CI/CD pipelines, and deployment processes that support both our core products and emerging AI-driven capabilities. Working closely with Engineering, QA, Product, and AI teams, you will help ensure the reliability, security, and performance of our services while driving operational excellence and continuous improvement across our technology stack.

Responsibilities

• Design, implement, and maintain CI/CD pipelines.

• Manage and optimize cloud infrastructure across AWS, Azure, and/or GCP.

• Develop and maintain Infrastructure as Code using Terraform.

• Manage Kubernetes-based environments and GitOps deployment workflows using Argo CD and Kustomize.

• Lead and support the migration of enterprise applications and infrastructure from on-premises environments to scalable SaaS and cloud-native architectures.

• Establish, maintain, and continuously improve production environments, ensuring high availability, security, scalability, and operational excellence.

• Demonstrate strong production ownership, including incident management, root cause analysis, capacity planning, and performance optimization.

• Collaborate with Engineering, QA, Product, and AI teams.

• Support the deployment, operation, and monitoring of AI and Generative AI services.

• Build and maintain monitoring, logging, and alerting systems.

• Troubleshoot and resolve infrastructure, deployment, and production issues.

Requirements

  • 5+ years of experience as a DevOps Engineer or similar
  • infrastructure-focused role.
  • Hands-on experience with Azure, Aws, or GCP.
  • Experience with CI/CD tools such as Jenkins, GitHub Actions, or similar platforms.
  • Strong knowledge of Terraform and Infrastructure as Code practices.
  • Experience with Docker, Kubernetes, Argo CD, and Kustomize.
  • Experience designing and operating production-grade Kubernetes saas platforms or enterprise environments.
  • Experience with monitoring and observability tools such as Prometheus, Grafana, and ELK.
  • Strong troubleshooting, analytical, and communication skills.
  • B.Sc. 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, including familiarity with MLOps concepts.
  • AI deployment platforms or cloud-based AI services.
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