our company's platform runs inside the security stacks of enterprise customers, processing live detection and telemetry data from SIEM, EDR, and SOAR systems at real scale. Until now, infrastructure has been built and carried by the founding engineering team alongside everything else. You will be the first dedicated owner of it. That means designing our company's cloud foundation across GCP and AWS, building the deployment, scaling, and observability layers from the ground up, and setting the standards the rest of engineering builds on. It also means holding a bar that most startups can defer and our company cannot: we sell to security teams who audit their vendors seriously, so how we run our own infrastructure is part of how we win. This is a hands-on senior role with real architectural authority, and as engineering grows you become the person others learn cloud and infrastructure from.
What you'll be doing
Multi-cloud architecture: Own the design and implementation of our company's multi-cloud architecture across GCP and AWS, including the cross-cloud standards, boundaries, and tradeoffs that keep two providers from becoming twice the complexity.
Infrastructure as code: Build and maintain the entire environment in Terraform, so infrastructure is reviewable, reproducible, and scales without a person in the loop.
Deployment pipelines from scratch: Create the CI/CD foundation that lets a small team ship quickly and safely, with the testing, gating, and rollback paths that make fast releases boring rather than risky.
Kubernetes at data scale: Run and scale the Kubernetes and containerized workloads behind our company's security data processing, where volume, latency, and cost pressure all show up at once.
Security of our own stack: Own identity, access, secrets, network boundaries, and infrastructure hardening to a standard that holds up under customer security review.
Reliability and visibility: Build the logging, monitoring, and alerting layer that tells us something is wrong before a customer does, and make on-call sustainable as the system grows.
Technical direction: Partner directly with the founding engineering team on architectural decisions, and raise the infrastructure and cloud fluency of every engineer who joins after you.

We're looking for a Senior Platform Engineer to join our CTI Group. We're a startup that builds a lot, so you'll work across a wide range of systems and tools, build new platforms and services, and have real influence over where the platform goes next.
You'll work on the platform behind our CTI products: the microservices running on Kubernetes, the cloud and on-prem environments around them, and the infrastructure behind the systems. There's a lot still to build, and you'll have a real say in what comes next.
Responsibilities
Own what you build end to end, from design through implementation to running it in production.
Design and build the internal platforms, tools, and services other engineering teams rely on, and add new ones as we grow.
Make architectural decisions across infrastructure, applications, networking, security, and data.
Design, build, and maintain our cloud and on-prem environments.
Build and operate the infrastructure behind our AI/ML systems at scale.
Manage and evolve our Kubernetes environments.
Build and maintain CI/CD pipelines, deployment automation, and Infrastructure-as-Code with Terraform.
Build security into the platform, from IAM and secrets management to network policies.
Set up the monitoring and observability that show us how the platform is doing.
Design for scalability, availability, performance, and resilience in high-scale production systems.
Apply Infrastructure-as-Code practices (Terraform) end to end.

We are seeking a DevOps software engineer for our agentic AI team. If you are excited to work at the frontier of AI agents and want your work to multiply the output of hundreds of engineers, we want to hear from you.

What you'll be doing:

Build AI agents that solve real engineering problems across the networking stack, with a focus on the infrastructure and services that bring them into reliable daily use.

Design, build, and operate the platforms that make those agents reliable: MCP tools, service APIs, cloud and on-prem services, and the systems behind them.

Work broadly across networking architecture teams to identify high-impact AI use cases and turn them into working solutions.

Drive rapid and responsible AI adoption across the organization: establish best practices, guardrails, and evaluation methods so teams embrace agentic workflows with confidence.

Build evaluation and observability pipelines that measure AI-agent behavior and platform reliability on real production workloads.

Required DevOps Engineer
About the role:
Our DevOps team operates the infrastructure that powers our AI and Computer Vision platform across construction sites in 15+ countries. From data pipelines and ML workloads to backend services - you'll work with a diverse, modern, Kubernetes-based stack and have real influence on how we build, deploy, and operate.
What you'll do:
Own Multi-Cloud Infrastructure: Work alongside the team to design, scale, and operate our high-scale, multi-region production infrastructure across AWS and GCP, powering construction sites globally.
Drive Kubernetes at Scale: Manage and evolve our Kubernetes platform on EKS and leveraging GitOps practices with ArgoCD and Helm to enable safe, fast, and reliable deployments.
Build Robust CI/CD: Design and maintain CI/CD pipelines that empower dozens of engineers to ship confidently - with automation, testing, and progressive delivery built in.
Tackle Diverse Infrastructure Challenges: Work hands-on with a wide variety of workloads - from heavy data processing and Computer Vision pipelines to backend services and ML inference - each with unique scaling, performance, and reliability requirements.
Ensure Reliability & Observability: Build and maintain world-class observability (metrics, logs, tracing, alerting) so that issues are caught early and resolved fast. Performance, reliability, and scalability are at the core of what you do.
Security & Cost: Partner with the team to strengthen our security posture, identity and access management, compliance, and cloud cost optimization across both clouds.
Ownership from 0 to 1: You will have real influence over our architecture and tooling. We want engineers who care about shaping what we build and how we build it, ensuring performance, security, and observability are baked in from day one.

Required Senior Staff Devops Engineer
Your Career:
We are establishing a new role focused on bridging AI capabilities with our internal DevOps platform. You will build AI-powered tools and services that run on top of the DevOps platform developed by the infrastructure team. This role combines hands-on DevOps/platform engineering work with close collaboration with software development teams, enabling them to build, deploy, and operate AI solutions efficiently, securely, and in alignment with organizational standards. You will act as a technical enabler, helping teams bring AI-driven applications into production while ensuring best practices across architecture, compliance, DevSecOps, FinOps, and AI governance. The role also includes direct contributions to the platform itself.
Your Impact:
Enhance DevOps platform capabilities to support AI-based tools and workloads.
Work closely with development teams to enable and support AI application delivery end-to-end.
Assist in designing architecture and production-grade implementation of AI systems.
Lead and enforce DevSecOps practices, compliance requirements, and governance standards for AI solutions.
Develop platform components and internal services as part of the AI infrastructure layer.
Support production readiness, monitoring, performance, reliability, and cost optimization (FinOps).
Serve as a technical interface between multiple engineering teams.
Participate in future on-call rotations.

We're looking for a Senior DevOps Engineer with a strong Developer Experience (DevEx) mindset, someone who genuinely enjoys making developers more productive by building and improving the tools, infrastructure, automation, and workflows they use every day.
You will work across CI/CD, developer tooling, automation, infrastructure, observability and productivity. Your primary focus will be making development and delivery faster, simpler, more reliable, and more cost-effective.
We need someone who can independently identify developer pain points, proactively discover opportunities for improvement, and turn them into scalable engineering solutions. You will be expected to measure the impact of your work and continuously improve critical metrics such as CI feedback time, pipeline reliability, deployment time, infrastructure cost, and overall developer productivity.
This role is a perfect fit for someone who thinks far beyond just "keeping the infrastructure running" and is excited about revolutionizing the entire engineering experience.
Your Impact:
You will tackle high-impact engineering challenges across our platform. Representative areas you will own and work on include:
Optimize CI/CD Pipeline Velocity & Stability:
Improve CI Stability: Leverage AI to automatically detect recurring test failures, dynamically disable problematic tests, and open Jira tickets for follow-up.
Automate Workflows: Utilize AI to automate merge conflict resolutions and keep the merge train moving smoothly.
Accelerate CI Velocity: Architect workflows to parallelize work both across jobs and within jobs. Introduce highly effective caching strategies and local Artifactory mirrors to drastically reduce build and dependency-fetch times.
Optimize CD: Reduce container image sizes and accelerate deployment times. Partner with QA to introduce and streamline automated regression testing against tenants.
Drive Repository Security & Maintenance:
Improve Security Posture: Automate the identification and remediation of Go and Python dependencies with known CVEs.
Automate Maintenance: Design systems to automatically handle Go and Python version upgrades across the codebase.
Ensure Performance: Automate performance testing to proactively detect and alert on performance regressions before they hit production.
Cloud Infrastructure & Cost Engineering:
Reduce Infrastructure Costs: Identify and implement innovative opportunities to reduce overall infrastructure and resource consumption.
Resource Efficiency: Introduce mechanisms to make our "slim tenants" even more resource-efficient for development and testing.
Smart Observability: Re-architect our logging strategy by streaming GCP logs to more cost-effective sinks instead of relying exclusively on Cloud Logging.
IaC Governance: Act as a gatekeeper for infrastructure quality by reviewing Terraform Merge Requests (MRs) for correctness, efficiency, maintainability, and strict adherence to engineering standards.

Principal DevOps Engineer (Cortex Agentix Endpoint Security)
Your Career:
Own and continuously improve AWS production infrastructure for scalability, reliability, security, performance, and cost.
Run and evolve Kubernetes environments that support fast, safe product delivery.
Drive developer velocity and production safety through better CI/CD pipelines, release workflows, deployment visibility, and GitOps practices.
Improve observability and incident response - reduce alert noise and raise signal quality.
Design and ship AI-assisted operational agents that change how engineers work - triaging monitoring alerts, summarizing incidents, proposing fixes, onboarding new services, answering questions and requests. This is a core part of the role, not a side project.
Build automation and self-service tooling that removes manual work from provisioning, monitoring, incident response, and developer workflows.
Analyze operational data across incidents, alerts, deployments, infra health, and cost to find reliability gaps, inefficiencies, and automation opportunities.
Partner with engineering, security, product, and leadership to remove bottlenecks and support safe production growth.
Evaluate and introduce new tools and AI-assisted approaches, balancing innovation with reliability, cost, and operational simplicity.
Your Impact:
You'll help scale production systems, improve deployment velocity and reliability, reduce operational overhead, and build automation and AI workflows that help engineering teams move faster and operate more efficiently.
This role is a strong fit for someone who enjoys ownership, collaboration, and operational innovation.

Our platform turns paperwork-based processes into revenue-generating customer engagements for some of the largest financial institutions in Israel, the US, Europe, and APAC.

Behind that surface, our infrastructure is split the way our customers are. Enterprise customers demand isolation and get it: a dedicated single-tenant environment each, down to their own compute, data stores, identity provider, and sometimes encryption keys we cannot access. Lower-tier customers run on shared multi-tenant infrastructure. More than fifty production environments across six AWS regions, all from one GitOps pipeline.

That split is what makes this role heavy. On the shared platform, a single change is instantly global; on the dedicated fleet, it must land correctly everywhere before anyone feels it. Every new customer adds load and cost in a straight line - your job is to break that line, making the platform carry its own weight through self-service, automation, and AI agents, not more hands. Nobody sits between you and the customer.

Responsibilities

Both tenancy models, as one system. Terraform, Helm, and ArgoCD delivering to shared and dedicated environments alike. You own the pipeline and the guardrails on it: progressive rollout, blast-radius containment, and drift detection that catches a mistake before a customer does.
Efficiency and cloud cost. Dedicated infrastructure per customer makes unit economics an engineering concern, not a quarterly finance exercise. You own attribution, right-sizing, spot strategy, and autoscaling that actually scales down - and you defend efficiency as a design constraint on every new deployment.
Turning operations into product. Onboarding, version rollouts, and config changes are tickets today; they should be self-service and boring. You decide what gets automated, what goes back to the owning team, and what still needs an expert.
Scale under real load. Temporal, KEDA queue-based autoscaling, and Karpenter are live and still maturing. You own how the platform grows and shrinks against real traffic, and what that costs per customer.
Security engineering for regulated customers. KMS-based key management, SIEM pipelines, WAF policy, certificate and secret lifecycle, per-customer SSO, vulnerability remediation, and turning penetration test findings into shipped fixes. Our customers are banks, insurers, and health funds. Their auditors are effectively part of our roadmap.
AI-driven operations. Our infrastructure repositories are already built for agentic work, with an internal harness of shared skills and agents. You inherit it, extend it, and push it into places it does not reach yet: triage, rollout verification, cost anomaly investigation, log analysis, and the long tail of operational work that has never been worth a script.
Reliability of what you ship. You are on call for your own systems, paged through PagerDuty against alerts you own.