Agentic AI Engineer - Product Support
Cato Networks Open · verified Oct 22, 2026 At a glance
Mid-level AI Engineering role at Cato Networks. Tel Aviv District.
Pay not stated AI Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Find support-team processes that can be automated or improved, then turn those needs into AI solutions
- Build agent workflows, LLM tools and retrieval pipelines that connect company data to internal processes
- Check source data for accuracy and gaps so AI systems have a reliable foundation
- Move solutions from initial concept through prototype and into production, with security controls and human review
- Track errors, hallucinations and performance changes, and document workflows for teams using them
What you bring
- At least three years developing software, including maintaining and delivering code for production
- Two or more years building generative AI solutions, with a strong focus on AI and machine learning
- Strong Python ability and a focus on production-ready work
- Practical knowledge of LLMs, SLMs, prompt and context design, and agent-based systems
Who this fits
This role suits a hands-on engineer who wants to ship practical AI systems rather than focus on research. You’ll work directly with business teams and explain AI capabilities and limits to internal users.
From the employer
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.
We're looking for a hands-on Agentic AI Engineer to build practical, high-impact AI solutions for our Product Support organization.
This is not a research role. You'll design and ship real AI systems that improve support workflows and processes - building custom solutions where off-the-shelf tools fall short. The role also requires close collaboration across teams: aligning with different stakeholders' needs and priorities, and building strong, ongoing partnerships across the organization.
Responsibilities:
- Partner directly with business teams to identify automation and optimization opportunities
- Design and implement agent-based AI workflows to automate internal processes end-to-end
- Design and build LLM-powered tools (agents, workflows, copilots) using frameworks like LangGraph / LangChain
- Develop RAG pipelines, integrate multiple data sources, and build intelligent automation flows
- Deep-dive into company data - validate quality, uncover gaps, and ensure AI solutions are built on solid foundations
- Take solutions from idea → prototype → production
- Governance, Reliability & Security
- Ensure AI workflows comply with security, privacy, and compliance requirements
- Implement guardrails, approvals, logging, and human-in-the-loop mechanisms where needed
- Monitor AI performance, errors, hallucinations, and drift
- Collaboration & Enablement:
- Partner with business owners to identify automation opportunities
- Translate business requirements into AI-driven solutions
- Document AI flows, decision logic, and operational runbooks
- Educate internal teams on AI capabilities and limitations
Requirements:
- 3 years of hands-on software development experience, including writing, maintaining, and delivering production-quality code
- 2+ years of GenAI development experience, with strong AI/ML focus - MUST
- Strong Python skills and production mindset - MUST
- Hands-on and deep understanding with LLMs, SLMs, prompt engineering, context engineering and agent-based systems
- Experience with LangGraph, crewAI, Strands or similar orchestration frameworks
- Experience in enterprise grade agentic solutions and bringing Agents into production
- Experience in AWS Agent Core - Advantage
- Solid understanding of ML workflows and MLOps principles
- Strong analytical skills and ability to work directly with non-technical stakeholders
- Builder mindset: proactive, independent, and impact-driven
- Experience with Agentic AI driven applications like n8n, UiPath, Make –Advantage
- Experience or familiarity with the Support domain – Advantage