Top Cybersecurity Jobs in Israel

As a Senior Back End Engineer, design and optimize backend systems for AI-driven security tools, ensuring scalability and security compliance.

As an Applied AI Engineer, you will develop machine learning models, integrate AI tools into workflows, and analyze security risks utilizing AI.

Take ownership of customer issues: diagnose, troubleshoot, and resolve technical problems in containerized environments. Coordinate with Sales, Professional Services, and Product teams, follow escalation procedures, and document reproducible steps and findings for enterprise customers.

The Product Operations specialist will optimize workflows, collaborate on product metrics, drive efficiency, and integrate AI tools within the product lifecycle.

Own the technical sales cycle from demo through POC to technical win. Deliver tailored demos, engage customer security and engineering teams, provide architecture guidance, support technical validation and objections, and act as a bridge to Product & Engineering. Represent OX at customer meetings and events.

As a Senior Full Stack Engineer, you'll design, build, and scale core systems, develop features, and integrate APIs in a collaborative environment.

Lead architecture and delivery of Aqua's AI products and shared AI capabilities across SaaS and on-premises. Design scalable backend services, APIs, data pipelines, model integration, and web experiences. Ensure reliability, security, observability, and operational simplicity. Provide hands-on technical leadership, review designs, evaluate technologies, and define architecture standards and reusable patterns.

As a Customer Success Engineer, you will onboard, implement, and optimize the Akeyless platform for customers while providing technical support and driving product adoption.

Design and build scalable backend integration capabilities with third-party SaaS, identity, PAM and SIEM systems; create robust integration architecture, monitoring, and performance tooling; deliver production-grade features from definition to client delivery; and design autonomous agentic systems to automate integrations.

Lead pre-sales technical discovery and architecture for enterprise customers, deliver demos, build POCs, influence product roadmap, enable partners, and produce reusable reference architectures and playbooks to support strategic security and identity deployments.

Lead the creation of an AI-native growth marketing engine for a B2B cybersecurity SaaS. Build integrated demand-generation campaigns, own full-funnel pipeline and messaging, coordinate with product, sales, and SDRs, and use data/AI to iterate and scale marketing impact toward revenue outcomes.

Lead advanced penetration tests targeting web applications and APIs, develop security tools, and author technical reports on vulnerabilities.

Outbound-focused SDR embedded in the sales process for a cloud security CNAPP. Researches and engages prospects, runs technical discovery, qualifies pipeline, supports AEs through early scoping, and applies qualification frameworks to progress enterprise opportunities.

Cynet is dedicated to simplifying security for organizations with an AI-driven platform for threat detection and response, providing support from security experts.

As a CyOps Analyst, you will investigate security alerts, conduct threat analysis, respond to incidents, and enhance threat hunting tools.

Design end-to-end user experiences for Sweet Security's cloud and AI security platform. Lead discovery, research, prototyping, and validation; create wireframes, flows, and high-fidelity designs in Figma; collaborate with PMs, engineers, and customers; conduct usability testing; evolve the design system; and balance user needs with technical and business constraints.

The Senior AI Engineer will develop full stack software components, lead design and code reviews, and improve system efficiency and stability.

Design, build, and operate scalable ML pipelines and infrastructure for training, fine-tuning, evaluating, deploying, and monitoring NLP and LLM models. Implement LLM-focused CI/CD, optimize runtime inference for low-latency/high-throughput, manage training environments and model registries, and ensure production performance, cost efficiency, and observability.

Serve as primary liaison for Defence clients, build trusted stakeholder relationships, manage high-value contracts, ensure compliance with defence regulations and security requirements, lead governance and status meetings, and align commercial and operational delivery with long-term account strategy.

Technical lead for a 6-engineer team building and operating complex, large-scale LLM-powered security platform components. Own architecture, design docs, delivery, on-call operations, and mentor engineers while turning ambiguous AI/agent problems into shippable solutions.

Manage and optimize the marketing tech stack and lead lifecycle, build reporting and dashboards, ensure data quality and governance, streamline campaign operations and automation, partner with demand gen and sales on ABM and campaigns, drive conversion optimization, and support budget tracking and ROI measurement.

Design, deploy, and operate AWS-based infrastructure using Kubernetes and Helm; automate workflows and CI/CD pipelines; manage edge security (WAFs, gateways, load balancers); improve reliability through monitoring, incident detection, troubleshooting, and automated recovery; build tooling and infrastructure-as-code with Terraform.

Design, build, and maintain scalable data warehouse infrastructure and pipelines. Enable analytics and product teams via data modeling, BI dashboards, data governance, and quality monitoring.

Lead product vision and execution for Noma's AI Security platform. Own end-to-end product strategy, prioritize roadmap, write PRDs, collaborate with engineering/design/data/security/GTM, drive customer-facing evaluations/POCs, and deliver features for data protection, AI governance, observability, and secure AI operations.

Build and operate scalable backend services for an AI security and governance platform. Collaborate across product, research, and platform teams, integrate with AI/LLM platforms and cloud providers, own architecture decisions, implement testing and observability, and troubleshoot production issues to improve reliability and resilience.