Expert DevOps Engineer

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Radware develops cybersecurity and application delivery solutions for physical, cloud, and software-defined data centers. Our Cloud R&D team builds and operates security services that use large-scale distributed systems, big data analytics, machine learning, and behavioral analysis to detect and mitigate cyber threats.

We are looking for a DevOps Expert / Technical Lead to design, build, and operate the infrastructure behind these services across AWS, Azure, and GCP.

This is a hands-on individual contributor role with responsibility for providing technical direction across teams. You will lead infrastructure projects, troubleshoot complex production failures, and guide engineers through architecture and implementation decisions. Direct people management is not required.

You will also build AI-assisted engineering and operational workflows, test their behavior, and define how they can safely interact with infrastructure.

What you will do:

• Coordination Design cloud infrastructure for distributed security services that must remain available across regions and cloud providers.
• Lead infrastructure projects from design and proof of concept through deployment and production operation.
• Build and maintain Kubernetes platforms, reusable infrastructure modules, and automated environment provisioning.
• Develop CI/CD and GitOps workflows with automated verification, progressive delivery, and rollback.
• Diagnose production failures across application, container, operating system, network, and data layers.
• Use performance and capacity measurements to improve scalability, reliability, and cloud costs.
• Design and test backup and restore procedures, regional failover, and disaster recovery.
• Set engineering standards for infrastructure code, testing, observability, secrets management, and deployment security.
• Build self-service tools and APIs that reduce manual infrastructure work for development teams
• Implement AI-assisted workflows for infrastructure reviews, pipeline troubleshooting, incident evidence collection, and runbook execution.
• Connect AI tools to engineering systems through APIs and controlled access, with scoped permissions, audit trails, and approval steps where needed.
• Test AI workflows against realistic tasks, incorrect outputs, and tool failures. Measure whether they reduce work or improve results.
• Lead design reviews, document architectural decisions, and mentor engineers through implementation and troubleshooting.

Requirements

• 9+ years of experience in DevOps, SRE, platform engineering, or infrastructure software engineering.
• Experience leading significant infrastructure projects and getting technical designs adopted across teams.
• Deep production expertise in at least two of AWS, Azure, and GCP, with working knowledge of the third.
• Strong knowledge of cloud networking, IAM, managed services, high availability, and cost optimization.
• Deep Kubernetes experience, including networking, scheduling, resource management, scaling, upgrades, and failure diagnosis. Experience with managed platforms such as EKS, AKS, and GKE.
• Advanced Terraform or equivalent infrastructure-as-code experience, including reusable modules, state management, testing, and controlled migrations.
• Strong programming skills in Python or Go, alongside Bash, with experience building tested tools, services, or integrations.
• Deep Linux and networking knowledge, including TCP/IP, DNS, TLS, proxies, load balancing, and traAic routing.
• Experience designing CI/CD systems and GitOps workflows for multiple services and environments.
• Experience using metrics, logs, traces, and performance analysis to diagnose production problems.
• Experience implementing least-privilege access, secrets management, and automated security checks.
• Hands-on experience building an AI-assisted engineering or operational workflow. You should be able to explain its integrations, how you validated its output, how it handled failures, and what value it delivered.
• Ability to explain architecture trade-oAs, guide technical decisions, and help engineers solve unfamiliar problems.
• A bachelor’s degree in computer science, Software Engineering, or a related field, or equivalent practical experience.

An advantage

• Experience with high-throughput cybersecurity systems, multi-region platforms, or multi-tenant SaaS infrastructure.
• Operational expertise with Kafka, NATS, ClickHouse, PostgreSQL, or similar messaging and data technologies.
• Experience developing Kubernetes operators, controllers, or platform APIs.
• Experience integrating LLM APIs, tool-calling agents, or Model Context Protocol (MCP) into engineering workflows.
• Experience designing evaluations and access controls for AI-enabled operational tools.
• Familiarity with policy as code, software supply chain security, or operating ML inference services.
• Familiarity with 24/7 production support and on-call practices.