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.

Responsibilities

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.

Requirements

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.