Azure AI Engineer

Position Overview We are seeking a highly skilled Azure AI Engineer to join our team. The engineer will be responsible for designing, developing, and deploying AI‐powered applications on Microsoft's Azure ecosystem. This role will involve working with Azure AI Services, Azure OpenAI, Microsoft Copilot Studio, MCP (Model Context Protocol) services, and Power Platform integrations to deliver intelligent solutions, such as chatbots, knowledge agents, and adaptive multi‐LLM models. The engineer will also collaborate with the team to identify the appropriate technology stack, establish best practices, and contribute to building scalable, secure, and production‐ready AI solutions. Key Responsibilities Technology Stack Definition – Identify, evaluate, and recommend Microsoft and Azure‐based technologies for AI/LLM development, ensuring scalability and security. AI Application Development – Design and implement AI applications such as chatbots, conversational agents, and multi‐LLM orchestration using Azure AI and Copilot Studio. Multi‐LLM Integration – Implement provider‐agnostic orchestration and model routing across Azure OpenAI, OpenAI, and other providers (e.g., via Semantic Kernel hybrid model orchestration, LangChain routing, or LiteLLM). MCP Integration – Develop and manage MCP (Model Context Protocol) services for orchestrating responses across OpenAI, Azure OpenAI, and other LLM providers. End‐to‐End Deployment – Build, test, and deploy applications on Azure (VMs, AKS, Functions, App Services, etc.), ensuring reliability and high availability. Power Platform Integration – Extend applications using Power Apps, Power Automate, and Power Pages to connect AI services with business processes. Data & Knowledge Engineering – Integrate Azure Cognitive Services, Azure Search, Dataverse, and vector databases for knowledge ingestion, semantic search, and retrieval‐augmented generation (RAG). Data Pipelines – Build and manage Azure Data Factory, Synapse pipelines, or Fabric pipelines for scalable data ingestion and transformation. Collaboration & Documentation – Work closely with product managers, solution architects, and business stakeholders to translate requirements into technical solutions, and maintain technical documentation and reusable assets. Continuous Improvement – Stay updated with Microsoft's AI advancements (Azure AI Studio, Fabric, Synapse, Cognitive Services) and incorporate them into project designs. Requirements Required Skills & Qualifications Education – Bachelor's or Master's degree in Computer Science, AI/ML, or a related field. Experience – 3–6 years of experience in AI/ML application development with focus on Microsoft and Azure platforms. Certifications AI-102– Azure AI Engineer Associate or Azure Solutions Architect DP-100– Azure Data Scientist Associate AZ-204– Azure Developer Associate AZ-400– Azure DevOps Engineer Expert Hands‐on experience with Azure AI Services (Language, Vision, Speech, Cognitive Search, Azure OpenAI)Knowledge Microsoft Copilot Studio & Power Platform Azure Functions, AKS (Kubernetes), App Services, Logic Apps Dataverse, Azure SQL, and vector DBs (Pinecone, Cosmos DB with vectors, etc.)Azure Fabric Pipelines Programming background – Strong programming background in Python, C#, or Node.js for AI/LLM development. Multi‐LLM orchestration – Experience in multi‐LLM orchestration (e.g., MCP services, LangChain, Semantic Kernel). MLOps practices – Knowledge of MLOps practices, CI/CD pipelines in Azure DevOps or GitHub Actions. Enterprise security – Understanding of enterprise security, compliance, and data governance in Azure. Preferred Skills • Certifications in Microsoft Certified: Azure AI Engineer Associate or Azure Solutions Architect. • Experience integrating AI into enterprise systems (e.g., CRM, ERP, LMS). • Familiarity with RAG pipelines, prompt engineering, and fine‐tuning LLMs. • Design and manage Azure core infrastructure (networking, IAM, storage, monitoring) to support AI workloads. • Implement Azure Monitor, Log Analytics, and Application Insights to track performance, reliability, and cost of AI applications. • Ensure enterprise‐grade security with Azure Key Vault, Azure AD / Entra ID role‐based access, and policy enforcement. • Implement infrastructure‐as‐code using ARM templates, Bicep, or Terraform for repeatable deployments. • Exposure to Agile/Scrum methodologies for project delivery. #J-18808-Ljbffr