At Check Point Software Technologies, we secure the world - and now we're securing the AI revolution. We're building a Workforce AI Security Platform: a cloud-native, multi-tenant SaaS platform that governs, protects, and enables safe AI adoption across global enterprises. This is a greenfield opportunity to help architect the infrastructure backbone of a platform that will define how organizations adopt AI at work securely.

We're looking for a Senior DevOps Engineer who owns infrastructure end-to-end, ships with confidence, and raises the reliability bar without being asked. You will work closely with backend, full-stack, and security teams, as well as DevOps teams across other Check Point organizations, to build a highly available, reliable, and secure production environment. If you get energized by building systems that scale, pipelines that teams love, and platforms that never sleep - this role is for you.

• Own and evolve our cloud infrastructure across multi-region production environments, end-to-end.
• Lead our GitOps deployment model - designing and maintaining declarative, automated deployment workflows with zero manual gates.
• Build, maintain, and optimize CI/CD pipelines with a strong focus on developer experience, reliability, and speed.
• Initiate, implement, and champion an AI-first DevOps & SRE ecosystem - identifying opportunities, building AI agents and intelligent automation, and driving their adoption across engineering operations.
• Develop automation frameworks for provisioning, scaling, observability, and incident response, leveraging AI-powered tooling and agentic workflows to reduce toil.
• Operate and improve our observability platform: metrics, logs, alerting, dashboards, SLOs/SLIs, and on-call tooling.
• Champion zero-trust secrets management and credential-less authentication patterns across the stack.
• Partner with architects and engineering leadership on cloud cost optimization, availability, and performance.
• Build internal tooling and automation that multiplies engineering velocity across the organization.

• 5+ years of hands-on DevOps experience in a SaaS product environment - Must.
• Demonstrated initiative in applying AI to engineering operations - designing and building AI agents, agentic workflows, LLM-powered automation, or Model Context Protocol (MCP) integrations that reduced operational toil and improved production reliability, quality, or velocity - Must.
• Strong scripting and programming skills - Python and Bash for automation, tooling, and AI agent development; Go is a plus.
• Strong motivation to continuously learn and adopt emerging technologies, and to share that knowledge across the team.
• Deep, hands-on AWS expertise; multi-cloud (AWS, GCP, Azure) experience is a strong plus - Must.
• Strong understanding of containers and orchestration - Docker, Kubernetes, including workloads, networking, service mesh (Istio), Helm/Kustomize, and autoscaling (KEDA, HPA, VPA).
• Strong experience with:
  • Infrastructure-as-Code - Terraform, Crossplane, and/or cloud-native declarative tooling.
  • GitOps principles and tooling (ArgoCD or equivalent).
  • CI/CD platforms - building reusable, scalable, security-hardened pipeline templates (GitHub Actions or equivalent).
  • Secrets management - dynamic injection, IRSA/Workload Identity, avoiding long-lived credentials.
• Experience embedding security into CI/CD: vulnerability scanning, SBOM generation, and supply chain security (Trivy, Grype, Syft, JFrog Xray).
• Solid observability knowledge - OpenTelemetry, Prometheus, Grafana, Datadog, ELK/OpenSearch, distributed tracing.
• Hands-on experience with AI/ML workloads or LLMOps infrastructure - a significant advantage.
• Cost-awareness (FinOps) - treating cloud spend as a core engineering metric.
• Clear communication skills - able to align engineers, security teams, and leadership around infrastructure decisions.
• A strong sense of ownership - proactively identifying gaps and driving improvements.