Skai
Skai
Skai is a B2B marketing technology company that helps brands manage advertising across commerce media. Its omnichannel platform brings retail media networks, search, and social advertising into one interface, giving marketers a consolidated view of campaign performance and investment. AI-powered tools turn advertising data into actionable strategies, helping teams optimize media programs, reduce fragmentation, and support brand growth across digital commerce channels.

Senior DevOps Architect at Skai — Hybrid Israel

Lead DevOps and developer experience architecture for Skai’s AI-powered omnichannel marketing platform. Shape cloud infrastructure, self-service engineering platforms, and reliable production AI services across teams.

Description

  • Own the DevOps and developer experience architecture roadmap.
  • Set shared engineering standards and guide initiatives from design to production through hands-on delivery.
  • Design scalable, secure, resilient cloud infrastructure across Kubernetes, networking, infrastructure as code, and modernization.
  • Build shared platforms and self-service workflows for development, CI/CD, deployment, and production troubleshooting.
  • Establish architecture standards for observability, availability, disaster recovery, and capacity planning.
  • Design and help deliver production AI services and agents, including integration, evaluation, security, and operational reliability.
  • Coordinate priorities across R&D, Product, and Security.
  • Manage cross-team dependencies while mentoring engineers and technical leads.

Requirements

  • At least 3 years as a DevOps Architect, owning architecture and leading technical initiatives across multiple teams.
  • At least 8 years of experience in infrastructure, DevOps, platform engineering, or software engineering.
  • At least 5 years of hands-on programming experience with Python, Go, Java, or comparable languages.
  • Experience designing and maintaining production services, APIs, or shared engineering tools.
  • Experience architecting distributed production systems, defining service boundaries, evaluating technologies, documenting trade-offs, and leading implementation and migration.
  • Deep expertise in at least one major cloud platform: AWS, GCP, or Azure.
  • Hands-on experience with Kubernetes, Terraform or comparable infrastructure-as-code tools, and CI/CD or GitOps platforms such as Jenkins, GitHub Actions, or Argo CD.
  • Experience creating developer platforms, reusable infrastructure components, and self-service workflows used by multiple engineering teams.
  • Strong knowledge of networking, cloud security, observability, high availability, and disaster recovery.
  • Experience operating and troubleshooting production systems.
  • Demonstrated experience taking AI services and agents into production, including model integration, orchestration, tool calling, evaluation, and connections to internal or external systems.
  • Experience gathering technical requirements, presenting architecture proposals, and guiding cross-team decisions through implementation and adoption.
  • Experience designing and operating data infrastructure with Kafka, Airflow, Spark, or similar technologies is an advantage.
  • Experience with database, data warehouse, or data lake architecture and operations—including performance tuning, capacity planning, backup and recovery, and data reliability—is an advantage.
  • Experience building shared AI platforms or delivering internal automation agents and customer-facing AI services is an advantage.

Benefits

  • Hybrid work combining home and office work.

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