Cato Networks is the converged network and security cloud for the AI era.
As AI reshapes cybersecurity, enterprises face a dual challenge: unlocking AI’s potential while defending against AI-driven attacks. Cato addresses both through a unified, cloud-native platform, providing the visibility and context to prioritize risks and stop threats in real time across users, applications, data, and AI agents.
Join us as we scale: take on ambitious technological challenges and shape the next generation of enterprise security at a company trusted by thousands of organizations worldwide.
Welcome to the future of cloud networking and security!
Cato Networks is the first company to converge enterprise networking and security into one centralized and global service delivered by cloud. It is led by networking and security pioneer Shlomo Kramer (Check Point, Imperva) and an early investor in Palo Alto Networks, Exabeam, Trusteer and more. Cato's unique technology inspired a brand-new product category, later named "SASE" by Gartner — a market expected to reach $28.5 billion by 2028. This is your opportunity to get on the rocket ship and join a company building a cutting-edge enterprise network and secure cloud platform, on a fast track to becoming the worldwide market leader — don't miss it!
About the Team
The AI Enablement group leads Cato's cross-R&D AI initiatives — AI onboarding and developer enablement, shared AI infrastructure and tooling, optimization of existing systems, evaluation and adoption of new AI technologies, and other strategic AI efforts. We sit at the center of R&D and partner with every engineering team to raise the bar on how the entire organization builds with AI.
We're looking for a Staff Software Engineer to build and evolve Cato's shared AI platform: the backend services, infrastructure, and tooling that power AI adoption across R&D. You'll own systems from architecture through production operations, ship AI solutions end-to-end, and work closely with engineering teams to help hundreds of developers build with AI.
Responsibilities
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Partner directly with teams across R&D to identify high-impact opportunities to apply AI — in developer workflows, internal tooling, and product engineering..
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Build and evolve Cato’s AI platform end-to-end — from user interfaces and backend services to cloud infrastructure, deployment, and production operations..
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Help build Cato’s AI software factory — orchestrating AI agents, tools, and workflows to take software from idea to production and transform how 600+ engineers build and ship..
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Drive AI onboarding and enablement: create guidelines, patterns, and best practices; educate teams on AI capabilities, limitations, and responsible use..
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Continuously evaluate and pilot new AI technologies, models, and tools, and bring the best of them into Cato's engineering practice..
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Take solutions from idea → prototype → production, owning quality, reliability, and iteration..
Requirements
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7+ years of hands-on software engineering, including writing, maintaining, and delivering production-quality code.
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Proven hands-on experience building, maintaining, and operating production systems on AWS, including system design, deployment, monitoring, troubleshooting, reliability, and scalability.
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Strong GenAI development experience: LLMs, prompt and context engineering, and agent-based systems.
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Experience with agentic frameworks and platforms (e.g. AWS Bedrock AgentCore, LangChain/LangGraph, or similar).
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Proven experience designing and building agentic AI workflows and LLM-powered applications, and bringing them to production.
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Solid grounding in APIs, integrations, databases, cloud environments, monitoring, logging, security, and deployment practices.
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Strong builder mindset: proactive, independent, hands-on, and impact-driven — you'd rather ship a prototype than write a spec about one.
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Excellent interpersonal and communication skills — this role touches every R&D team, so building trust, listening well, and translating between business and technical needs is essential.
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Genuine passion for AI: you love the domain, have a sharp sense for where it creates real impact, and you're constantly learning and experimenting with new technologies.
Nice to Have
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Experience building developer-facing tooling or platforms adopted across an engineering org.
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Experience with enterprise AI governance, evaluation frameworks, security, compliance, and privacy.
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Familiarity with the networking/security/SASE domain.
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Open-source contributions, side projects, or a track record of experimenting with the latest AI tooling