Microsoft Azure Agentic AI Solution Architect

What the role needs

• Job Description – Microsoft Azure Agentic AI Solution Architect
• Agentic AI & GenAI Architecture
• Design end-to-end Agentic AI architectures using Microsoft Foundry/Azure AI Foundry and Azure OpenAI.
• Define single-agent and multi-agent patterns including orchestration, planner/executor flows, memory, tool use, reflection, human-in-the-loop approvals, and agent handoffs.
• Design interoperable agent solutions using patterns and protocols such as MCP and A2A , where appropriate.
• Establish reusable architecture patterns for agents, prompts, tools, grounding, workflows, and enterprise integrations.
• Evaluate foundation models and orchestration approaches based on accuracy, latency, security, scalability, and cost.
• RAG, Knowledge & Data Architecture
• Architect enterprise RAG solutions using Azure AI Search and governed enterprise data sources.
• Define document ingestion, chunking, embeddings, hybrid/vector search, reranking, metadata filtering, and citation/source-traceability patterns.
• Design permission-aware retrieval and secure grounding across enterprise repositories and APIs.
• Apply semantic models, knowledge graphs, ontologies, and Microsoft Fabric capabilities where they add business value.

From the employer

Job Description – Microsoft Azure Agentic AI Solution Architect Position Summary We are seeking an experienced Microsoft Azure Agentic AI Solution Architect to lead the architecture, design, and delivery of enterprise-grade Agentic AI and Generative AI solutions on Microsoft Azure. The architect will translate business requirements into secure, scalable, governed, and production-ready AI architectures, taking solutions from discovery and proof-of-concept through industrialized deployment and operations. The role requires strong hands-on architecture expertise across Microsoft Foundry/Azure AI Foundry, Azure OpenAI, multi-agent orchestration, Retrieval-Augmented Generation (RAG), enterprise integrations, cloud-native Azure services, security, Responsible AI, and LLMOps/AgentOps . Key Responsibilities Agentic AI & GenAI Architecture • Design end-to-end Agentic AI architectures using Microsoft Foundry/Azure AI Foundry and Azure OpenAI. • Define single-agent and multi-agent patterns including orchestration, planner/executor flows, memory, tool use, reflection, human-in-the-loop approvals, and agent handoffs. • Design interoperable agent solutions using patterns and protocols such as MCP and A2A , where appropriate. • Establish reusable architecture patterns for agents, prompts, tools, grounding, workflows, and enterprise integrations. • Evaluate foundation models and orchestration approaches based on accuracy, latency, security, scalability, and cost. RAG, Knowledge & Data Architecture • Architect enterprise RAG solutions using Azure AI Search and governed enterprise data sources. • Define document ingestion, chunking, embeddings, hybrid/vector search, reranking, metadata filtering, and citation/source-traceability patterns. • Design permission-aware retrieval and secure grounding across enterprise repositories and APIs. • Apply semantic models, knowledge graphs, ontologies, and Microsoft Fabric capabilities where they add business value. Azure Cloud & Integration Architecture • Design secure cloud-native solutions using Azure Functions, Durable Functions, Container Apps/AKS, App Service, API Management, Service Bus, Event Hubs, Logic Apps, Storage, Cosmos DB/SQL, and related Azure services. • Integrate agents with Microsoft 365, Copilot Studio, Microsoft Graph, Power Platform, ServiceNow, enterprise APIs, SaaS platforms, and data services. • Define event-driven and API-led integration patterns for long-running and asynchronous agent workflows. Security, Governance & Responsible AI • Define identity and access architecture using Microsoft Entra ID, Managed Identity, RBAC/PIM, Key Vault, private endpoints, and least-privilege principles. • Establish guardrails for prompt injection, data leakage, unsafe tool execution, hallucinations, sensitive information, and high-risk actions. • Implement human approval and auditable execution for state-changing or sensitive agent actions. • Ensure alignment with enterprise security, privacy, compliance, Responsible AI, and architecture governance standards. AgentOps, LLMOps & Production Readiness • Define CI/CD and lifecycle management for prompts, models, agents, tools, knowledge assets, and configurations. • Establish evaluation frameworks covering groundedness, relevance, task success, safety, latency, reliability, and cost. • Design production observability using Azure Monitor, Application Insights, Log Analytics, tracing, metrics, and audit logs. • Define operational patterns for retries, idempotency, failure handling, circuit breakers, model routing, token optimization, drift detection, and cost governance. Architecture Leadership & Delivery • Lead discovery workshops, architecture assessments, solution design, PoCs, and production implementation roadmaps. • Produce architecture artifacts including HLD/LLD, integration designs, security models, deployment views, and architecture decision records. • Drive architecture reviews and collaborate with business stakeholders, engineering, data, security, infrastructure, DevOps, and operations teams. • Mentor engineering teams and establish reusable standards and engineering best practices for enterprise Agentic AI delivery. • Provide technical leadership from ideation through deployment and production support. Required Skills & Experience • 12+ years of overall technology experience with significant solution/enterprise architecture responsibility. • 5+ years designing solutions on Microsoft Azure, including cloud-native, integration, security, and data architectures. • Strong hands-on experience with Agentic AI, Generative AI, LLMs, RAG, and multi-agent architectures . • Experience with Microsoft Foundry/Azure AI Foundry, Azure OpenAI, Azure AI Search , and the Azure AI ecosystem. • Experience with agent frameworks or SDKs such as Semantic Kernel, LangGraph/LangChain, Microsoft agent technologies, or equivalent frameworks. • Strong understanding of MCP, A2A, tool/function calling, agent orchestration, memory, and human-in-the-loop patterns . • Strong Azure architecture experience across APIs, messaging, serverless/container platforms, identity, networking, storage, databases, and observability. • Knowledge of vector databases, embeddings, hybrid retrieval, reranking, knowledge graphs, and enterprise data governance. • Experience with Python and/or .NET/Java and REST/API-based integration. • Experience with Docker/Kubernetes, infrastructure automation, Azure DevOps/GitHub, and CI/CD. • Demonstrated ability to architect secure, resilient, scalable, observable, and cost-efficient production AI solutions. • Strong communication skills with the ability to engage both senior business stakeholders and engineering teams. Preferred Qualifications • Experience with Microsoft 365 Copilot, Copilot Studio, Microsoft Graph, Fabric IQ/Data Agents , or related Microsoft AI capabilities. • Experience with regulated or security-sensitive enterprise environments. • Familiarity with AI evaluation, red teaming, Responsible AI, LLMOps, AgentOps, and FinOps practices. • Experience modernizing enterprise applications and integrating AI agents with existing business processes and systems. • Microsoft certifications relevant to Azure and Agentic AI, such as AI-103, AB-100 , or equivalent architecture credentials, are desirable. Key Success Measures • Delivery of secure, scalable, production-ready Agentic AI solutions aligned to measurable business outcomes. • Reduction in time from AI experimentation to enterprise production deployment. • High levels of agent quality, groundedness, reliability, traceability, and operational observability. • Effective governance of autonomous actions, enterprise data access, security, and AI risk. • Creation and adoption of reusable Agentic AI architecture patterns and accelerators across delivery teams. Salary and Other Compensation: The annual salary for this position is between $150-160Kdepending on experience and other qualifications of the successful candidate. This position is also eligible for Cognizant’s discretionary annual incentive program, based on performance and subject to the terms of Cognizant’s applicable plans. Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements: · Medical/Dental/Vision/Life Insurance · Paid holidays plus Paid Time Off · 401(k) plan and contributions · Long-term/Short-term Disability · Paid Parental Leave · Employee Stock Purchase Plan Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable la Additional employment information Compensation information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law. Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview. Cognizant is an equal opportunity employer. Your application and candidacy will not be considered based on race, color, sex, religion, creed, sexual orientation, gender identity, national origin, disability, genetic information, pregnancy, veteran status or any other characteristic protected by federal, state or local laws. Applicants for US based positions must be currently authorized to work for any employer in the United States. The company is unable to provide employment-based immigration sponsorship for US based positions. If you have a disability that requires reasonable accommodation to search for a job opening or submit an application, please email CareersNA2@cognizant.com for roles based in the Americas or CareersIndia2@cognizant.com for roles based in India. About Cognizant: Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization’s unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant.