AI Team Lead - Cato Networks

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) 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 and IS teams 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 3+ years as a hands- on as lead engineer, solution architect, AI lead or similar role. 1-2+ year proven experience with AI solutions. Managerial Experience for small teams, either direct or matrix based. Strong hands-on software development experience, including writing, maintaining , and delivering production-quality code. Proven experience managing, mentoring, or technically leading engineers. Strong GenAI development experience with LLMs, SLMs, prompt engineering, context engineering, and agent-based systems. Strong Python skills and a production-focused engineering mindset. Experience designing and building agentic AI workflows, RAG pipelines, LLM-powered applications, copilots, or intelligent automation solutions. Experience bringing AI agents, GenAI applications, or automation solutions into production. Solid understanding of APIs, integrations, databases, cloud environments, monitoring, logging, security, and deployment practices. Ability to work directly with non-technical stakeholders and translate business needs into technical solutions. Experience with AWS AgentCore , n8n, UiPath, Make, Workato , or similar is an advantage. Experience with enterprise AI governance, security, compliance, and privacy requirements is an advantage. Strong builder mindset: proactive, independent, hands-on, business-oriented, and impact-driven.

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) 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 and IS teams 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

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) 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 and IS teams 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 3+ years as a hands- on as lead engineer, solution architect, AI lead or similar role. 1-2+ year proven experience with AI solutions. Managerial Experience for small teams, either direct or matrix based. Strong hands-on software development experience, including writing, maintaining , and delivering production-quality code. Proven experience managing, mentoring, or technically leading engineers. Strong GenAI development experience with LLMs, SLMs, prompt engineering, context engineering, and agent-based systems. Strong Python skills and a production-focused engineering mindset. Experience designing and building agentic AI workflows, RAG pipelines, LLM-powered applications, copilots, or intelligent automation solutions. Experience bringing AI agents, GenAI applications, or automation solutions into production. Solid understanding of APIs, integrations, databases, cloud environments, monitoring, logging, security, and deployment practices. Ability to work directly with non-technical stakeholders and translate business needs into technical solutions. Experience with AWS AgentCore , n8n, UiPath, Make, Workato , or similar is an advantage. Experience with enterprise AI governance, security, compliance, and privacy requirements is an advantage. Strong builder mindset: proactive, independent, hands-on, business-oriented, and impact-driven.