Senior DevOps Engineer

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.