Senior AI Engineer — AI Enablement Group

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 that is delivered by cloud. It is led by networking and security pioneer Shlomo Kramer (Check Point, Imperva) and early investor (Palo Alto Networks, Exabeam, Trusteer and more). Cato’s unique technology inspired a brand-new product category, later named “SASE” by Gartner and a market expected to reach $28.5 billion by 2028.

This is your opportunity to get on the rocket ship and join a company that is building a cutting-edge enterprise network and secure cloud platform, and is 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 Senior AI Engineer who is equal parts builder and force multiplier: someone who ships real solutions end-to-end and helps hundreds of engineers work smarter with AI. You'll write code, design architectures, prototype fast, and work shoulder-to-shoulder with teams across R&D to turn AI opportunities into production impact.

What You'll Do

• Partner directly with teams across R&D to identify high-impact opportunities to apply AI — in developer workflows, internal tooling, and product engineering.

• Design, build, and ship LLM-powered tools and agentic systems end-to-end.

• Build and maintain shared AI infrastructure, frameworks, and reusable components that let every R&D team move faster.

• Drive AI onboarding and enablement: create guidelines, patterns, and best practices; educate teams on AI capabilities, limitations, and responsible use.

• Continuously evaluate and pilot new AI technologies, models, and tools, and bring the best of them into Cato's engineering practice.

• Take solutions from idea → prototype → production, owning quality, reliability, and iteration.

• Establish guardrails, evaluation, monitoring, and human-in-the-loop mechanisms; track performance, errors, hallucinations, and drift.

• Ensure AI solutions meet security, privacy, and compliance requirements.

• Document architectures, decision logic, and operational runbooks so solutions are maintainable and adoptable.

What We're Looking For

• 5+ years of hands-on software engineering, including writing, maintaining, and delivering production-quality code.

• Strong GenAI development experience — LLMs, prompt and context engineering, and agent-based systems — with a proven track record of designing and building agentic AI workflows and LLM-powered applications and taking them to production.

• Proficiency across the languages used throughout Cato R&D — Java, TypeScript, and Python — with the ability to work comfortably in more than one. (Strong Python plus willingness to work in Java/TypeScript is fine.)

• Solid grounding in APIs, integrations, databases, cloud environments (preferably AWS), monitoring, logging, security, and deployment practices.

• Strong builder mindset: proactive, independent, hands-on, and impact-driven — you'd rather ship a prototype than write a spec about one.

• 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.

• 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

• Experience with agentic frameworks and platforms (e.g., LangChain/LangGraph, AWS Bedrock AgentCore, or similar).

• Experience building developer-facing tooling or platforms adopted across an engineering org.

• Experience with enterprise AI governance, evaluation frameworks, security, compliance, and privacy.

• Familiarity with the networking/security/SASE domain.

• Open-source contributions, side projects, or a track record of experimenting with the latest AI tooling.