Onsite AI Engineering Lead — Agentic Systems (Dubai)

Isa Cybersecurity Inc.; Dubai; On-site; AED 400,000 - 700,000; Full time

Job summary

Isa Cybersecurity Inc. in Dubai is seeking a Forward Deployed AI Engineer – Technical Lead to drive hands-on AI squad leadership on-site in the UAE. You will define strategy, own delivery, and build agentic AI systems that deliver measurable outcomes across security contexts.

You will collaborate with stakeholders, architect scalable AI solutions, and guide end-to-end implementation, including data ingestion, model integration, tooling, and governance for enterprise-grade deployments.

Qualifications

• 8+ years of experience building production-grade software with 4+ years in Generative AI/LLMs
• Experience designing and deploying agentic AI solutions
• Hands-on experience with MCP to connect AI agents with systems, tools, APIs and data
• Experience deploying containerized solutions in cloud environments with observability

Responsibilities

• Clarify business problems and define measurable outcomes for AI initiatives
• Lead end-to-end AI engineering efforts from discovery to production deployment
• Design multi-agent systems and tool-orchestration patterns for enterprise scale
• Ensure security, governance, and cost Optimization across AI solutions
• Mentor engineers and establish engineering standards and best practices
• Collaborate with product owners, AI architects and external providers

Skills

Hands-on leadership

AI engineering

Agentic AI

MCP integration

Vector databases

Cloud-native

CI/CD

API design

Tools

MCP (Model Context Protocol)

OpenAPI/REST

Vector search

Kubernetes

Job description

Isa Cybersecurity Inc. in Dubai is seeking a Forward Deployed AI Engineer – Technical Lead to drive hands-on AI squad leadership on-site in the UAE. You will define strategy, own delivery, and build agentic AI systems that deliver measurable outcomes across security contexts.

You will collaborate with stakeholders, architect scalable AI solutions, and guide end-to-end implementation, including data ingestion, model integration, tooling, and governance for enterprise-grade deployments.