Al Solution Architect

AI Solution Architect – Generative AI & Agentic AI We are looking for an experienced AI Solution Architect to join a major enterprise technology transformation programme in the UAE. This is a hands-on architecture role focused on designing and delivering scalable Generative AI, Agentic AI and intelligent automation solutions. The successful candidate will work closely with engineering, cloud and technology teams to translate business requirements into secure, production-ready AI architectures. Key Responsibilities: • Design end-to-end architectures for Generative AI applications, AI agents and intelligent automation solutions. • Develop scalable AI solutions using LLMs, multi-agent frameworks and modern cloud technologies. • Define architecture patterns for AI agents, workflows, reusable components and system integrations. • Design solutions incorporating RAG, knowledge retrieval and enterprise data platforms. • Integrate AI applications with APIs, enterprise systems and cloud infrastructure. • Establish architecture standards covering security, performance, scalability and cost optimisation. • Ensure AI solutions follow responsible AI, data privacy and security best practices. • Evaluate emerging AI technologies, frameworks and tools through technical assessments and proofs of concept. • Collaborate with engineering teams to support successful implementation and deployment. Required Skills & Experience: • 12+ years of overall technology experience, including significant experience in solution architecture. • Minimum 3 years of experience architecting Generative AI, LLM or Agentic AI solutions in production environments. • Strong understanding of AI agents, multi-agent architectures and workflow orchestration. • Experience with Azure OpenAI, Azure AI Foundry or similar cloud-based AI platforms. • Knowledge of RAG, vector databases, semantic search and knowledge management. • Experience designing API integrations, microservices and cloud-native architectures. • Understanding of Model Context Protocol (MCP), agent frameworks and AI orchestration technologies. • Strong knowledge of cloud security, identity management and enterprise architecture principles. • Experience with Azure or AWS cloud environments. • Ability to translate complex technical requirements into scalable solution designs. Desirable Skills: • Experience with LangGraph, Semantic Kernel or similar AI frameworks. • Knowledge of Agent-to-Agent (A2A) communication protocols. • Experience with Kubernetes, Terraform and DevSecOps. • Familiarity with AI governance, model risk management and responsible AI frameworks. • Previous experience delivering enterprise AI solutions within banking, financial services or other regulated industries. • Relevant architecture certifications such as Azure Solutions Architect, AWS Solutions Architect or TOGAF.

12+ years of overall technology experience, including significant experience in solution architecture. Minimum 3 years of experience architecting Generative AI, LLM or Agentic AI solutions in production environments. Strong understanding of AI agents, multi-agent architectures and workflow orchestration. Experience with Azure OpenAI, Azure AI Foundry or similar cloud-based AI platforms. Knowledge of RAG, vector databases, semantic search and knowledge management. Experience designing API integrations, microservices and cloud-native architectures. Understanding of Model Context Protocol (MCP), agent frameworks and AI orchestration technologies. Strong knowledge of cloud security, identity management and enterprise architecture principles. Experience with Azure or AWS cloud environments. Ability to translate complex technical requirements into scalable solution designs.

Design end-to-end architectures for Generative AI applications, AI agents and intelligent automation solutions. Develop scalable AI solutions using LLMs, multi-agent frameworks and modern cloud technologies. Define architecture patterns for AI agents, workflows, reusable components and system integrations. Design solutions incorporating RAG, knowledge retrieval and enterprise data platforms. Integrate AI applications with APIs, enterprise systems and cloud infrastructure. Establish architecture standards covering security, performance, scalability and cost optimisation. Ensure AI solutions follow responsible AI, data privacy and security best practices. Evaluate emerging AI technologies, frameworks and tools through technical assessments and proofs of concept. Collaborate with engineering teams to support successful implementation and deployment.