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AI Architect / Lead - Enterprise Generative AI (Azure)

chabeztechEast Lansing, MI

yesterday

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Computer Systems Engineers/ArchitectsDatabase ArchitectsComputer and Information Systems Managers

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About the role

Job Description: AI Architect / Lead – Enterprise Generative AI (Azure) Please double check you have the right level of experience and qualifications by reading the full overview of this opportunity below.
Location
Onsite: Lansing, Michigan
Role: AI Architect / Technical Lead
Work Model: Onsite
Technology Focus: Microsoft Azure, Azure AI Foundry, Agentic AI, RAG, MCP, Azure OpenAIJob Summary We are seeking an experienced AI Architect / Technical Lead to design, architect, and deliver enterprise-grade Generative AI solutions using Microsoft Azure AI technologies. The ideal candidate will have strong expertise in Azure AI Foundry, agentic AI architectures, Retrieval-Augmented Generation (RAG), multi-agent orchestration, enterprise integrations, and responsible AI implementation. The candidate will lead the development of secure, scalable, permission-aware AI assistants integrated with enterprise platforms such as SharePoint, ServiceNow, Microsoft Teams, and Microsoft 365 Copilot. This role requires strong hands-on engineering capabilities, architecture leadership, and experience delivering production-ready AI solutions with enterprise-grade security, governance, observability, and deployment practices.
Key Responsibilities:
  • Enterprise GenAI Architecture
  • Architect and deliver enterprise AI assistants using Azure AI Foundry and Responses API agents.
  • Evaluate Microsoft Copilot Studio and conduct proof-of-concept implementations or side-by-side technology comparisons where appropriate.
  • Define scalable, secure, and maintainable GenAI architectures aligned with enterprise standards.
Retrieval-Augmented Generation (RAG): Design and implement enterprise RAG pipelines using Azure AI Search. Implement hybrid search, vector embeddings, semantic ranking, and relevance optimization. Build permission-aware retrieval mechanisms with document-level access control inherited from source systems. Integrate enterprise content repositories such as SharePoint and digital asset management platforms.
Agentic AI and MCP Integrations: Engineer agentic workflows connecting AI agents with enterprise systems through MCP servers and tool integrations. Integrate platforms such as ServiceNow using secure APIs and MCP-based architectures. Implement Microsoft Entra ID SSO, OAuth, and on-behalf-of (OBO) authentication flows. Ensure end-to-end user identity propagation and authorization across agent interactions.
Multi-Agent Orchestration:
  • Design multi-agent architectures with intent classification, routing, delegation, and specialized sub-agents.
  • Implement predictable fallback, retry, regeneration, and error-handling strategies.
  • Develop coordinated agent workflows for enterprise automation and decision intelligence.
  • Conversation Management and MemoryDesign conversation state management and persistent memory using Azure Cosmos DB.Support context-aware multi-turn conversations while maintaining grounding, consistency, and session continuity.
  • Implement appropriate memory retention and lifecycle management strategies. AI Safety, Security, and Compliance
  • Implement configuration-driven AI safety and compliance controls.
  • Integrate Azure AI Content Safety and Prompt Shields.
  • Implement PII/PHI detection, masking, and sensitive information handling.
  • Develop sensitive-statement detection mechanisms, including HR escalation workflows where required.
  • Ensure enterprise security, privacy, responsible AI, and regulatory compliance.
Azure AI Engineering and Observability:
  • Develop using Azure SDKs across AI inference, search, storage, identity, and monitoring services. Implement application monitoring, distributed tracing, and observability using Open
  • Telemetry and Azure Application Insights.
  • Optimize performance, scalability, reliability, and operational costs.
Prompt Engineering and Evaluation: Design, maintain, and optimize prompt templates for different agent personas and business use cases. Implement prompt versioning alongside evaluation datasets. Establish automated evaluation frameworks for groundedness, relevance, safety, and response quality. Implement evaluation-gated releases with automated testing and security reviews before environment promotion. Deployment and DevOpsDeploy enterprise AI assistants to Microsoft Teams and Microsoft 365 Copilot. Provision Azure infrastructure using Terraform and Infrastructure as Code (IaC).Build and maintain CI/CD pipelines using Azure DevOps. Establish development, testing, staging, and production deployment processes. Required Technical Skills Microsoft Azure AI Foundry Azure OpenAI / Responses APIAzure AI Search Retrieval-Augmented Generation (RAG) Vector Search, Hybrid Search, and Semantic Ranking Agentic AI and Multi-Agent Orchestration Model Context Protocol (MCP) Microsoft Entra ID, OAuth 2.0, and OBO Authentication Azure Cosmos DBAzure AI Content Safety and Prompt Shields Python and Azure SDKsREST APIs and enterprise integrations SharePoint, ServiceNow, and Microsoft GraphOpenTelemetry and Azure Application Insights Terraform and Azure DevOpsMicrosoft Teams and Microsoft 365 Copilot
Preferred Qualifications: Experience designing and implementing enterprise-scale Generative AI platforms. Strong understanding of enterprise security, identity management, and access control. Experience building production-grade RAG and agentic AI applications. Knowledge of AI governance, responsible AI, and compliance frameworks. Experience with automated LLM evaluation, testing, and release management. Strong architecture documentation, technical leadership, and stakeholder communication skills.
Ideal Candidate: ProfileThe ideal candidate will be a hands-on AI Architect with the ability to translate enterprise business requirements into scalable GenAI solutions. xhyhwjd They should have experience leading architecture decisions, developing production-ready AI applications, integrating enterprise systems, and establishing security and governance standards for enterprise AI adoption.
Work Location: Lansing, Michigan (Onsite)
Position: AI Architect / Lead – Enterprise Generative AI (Azure)

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JOB OVERVIEW

Experience level

Manager

Location

East Lansing, MI

Occupation

Computer Systems Engineers/Architects

Industry

Computer Systems Design Services

Posted

yesterday

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