
Salt
Microsoft AI Engineer
- Contract
- Abu Dhabi, United Arab Emirates
- Experience 2 - 5 yrs
Job expiry date: 29/05/2026
Job overview
Date posted
14/04/2026
Location
Abu Dhabi, United Arab Emirates
Salary
AED 15,000 - 20,000 per month
Compensation
Comprehensive package
Experience
2 - 5 yrs
Seniority
Experienced
Qualification
Bachelors degree
Expiration date
29/05/2026
Job description
The Microsoft AI Engineer (Agentic AI / Azure AI Platform) is responsible for architecting and developing enterprise-grade agentic AI systems within the Microsoft AI ecosystem, focusing on scalable, secure, and production-ready implementations. The role involves building complex multi-agent systems using the Microsoft Agent Framework and integrating architectural patterns from Semantic Kernel and AutoGen. It includes implementing Agent-to-Agent (A2A) communication and Model Context Protocol (MCP) to enable dynamic discovery and invocation of enterprise tools by AI agents. The engineer will design and deploy Retrieval-Augmented Generation (RAG) pipelines using Azure AI Foundry IQ and Azure AI Search, supporting high-accuracy, multi-hop reasoning across enterprise data sources. The role also requires designing hybrid orchestration models that combine dynamic agentic reasoning with deterministic workflow orchestration. Additionally, the engineer will deploy and manage the Foundry Citadel Platform to ensure governance, cost control, observability, and compliance across AI workloads while leveraging Citadelās agent-level tracing and automated evaluation capabilities for real-time monitoring. The position includes developing custom user interfaces using React.js, Tailwind CSS, and Streamlit to enable human interaction, supervision, and control of AI agents, and integrating agentic workflows into platforms such as Microsoft Teams, web portals, and the VS Code AI Toolkit. The role is deeply embedded in the Azure AI ecosystem, leveraging Azure OpenAI Service, Azure AI Search, Cosmos DB, and governance tools such as Microsoft Purview and AI Content Safety.
Required skills
Key responsibilities
- Architect and develop enterprise-grade multi-agent AI systems using the Microsoft Agent Framework, integrating Semantic Kernel and AutoGen patterns for scalable agent orchestration
- Implement Agent-to-Agent (A2A) communication and Model Context Protocol (MCP) to enable dynamic discovery and execution of enterprise tools by AI agents
- Design and build Retrieval-Augmented Generation (RAG) pipelines using Azure AI Foundry IQ and Azure AI Search for multi-hop reasoning and enterprise knowledge retrieval
- Develop hybrid orchestration systems combining dynamic agentic reasoning with deterministic workflow orchestration for enterprise AI solutions
- Deploy and manage Foundry Citadel Platform ensuring governance, cost control, observability, and compliance across AI workloads
- Utilize Citadel agent-level tracing and automated evaluation tools to monitor AI performance, safety, and compliance in real time
- Develop responsive user interfaces using React.js, Tailwind CSS, and Streamlit to enable human supervision and interaction with AI agents
- Integrate agentic AI workflows into enterprise platforms including Microsoft Teams, custom web portals, and VS Code AI Toolkit environments
Experience & skills
- Strong experience in Microsoft AI ecosystem including Azure AI Foundry, Azure OpenAI Service, and Azure AI Search
- Advanced programming skills in Python (FastAPI, Pydantic) and/or C# (.NET 8+)
- Hands-on experience with Microsoft Agent Framework, Semantic Kernel, or AutoGen for building agentic systems
- Strong knowledge of vector and hybrid search systems including Azure AI Search and Cosmos DB integration
- Experience with Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication architectures
- Familiarity with AI governance and safety tools such as Microsoft Purview, Citadel platform, or AI Content Safety
- Frontend development experience using React.js, Tailwind CSS, and real-time streaming LLM interfaces
- Ability to design and implement scalable, production-grade AI systems with focus on security, observability, and enterprise integration