Define reference architecture for agentic AI solutions across Copilot Studio and Azure AI Foundry

Establish design patterns for:

• Retrieval-Augmented Generation (RAG)

• Multi-agent orchestration

• Enterprise integrations (SAP, Salesforce, Databricks, SharePoint, Azure Ecosystem)

Guide use-case prioritization and platform selection (Studio vs Foundry vs hybrid)

AI Agent Development & Delivery:

Build and deploy production-grade AI agents, including:

• Knowledge & troubleshooting agents

• Operational / workflow automation agents

• Data and analytics-driven agents

• Implement:

• Prompt engineering and evaluation strategies

• Agent workflows and orchestration logic

• API, connector, and system integrations

Platform Foundation & Governance:

Establish enterprise AI guardrails, including:

• Security, RBAC, and identity integration (Entra ID)

• Data access boundaries and governance

• Audit logging and monitoring frameworks

• Define and implement:

• Agent lifecycle (draft pilot production retirement)

• CI/CD pipelines and deployment standards

Azure AI & Microsoft Ecosystem Implementation:

Configure and deploy:

• Azure OpenAI / model endpoints

• Azure AI Search (vector + semantic retrieval)

• Application Insights / Log Analytics monitoring

• Build and support:

• Copilot Studio environments and orchestration layers

• Azure AI Foundry-based custom agent services

Co-Development & Enablement:

Work directly with consultants and internal teams to:

• Co-build pilot agents

• Facilitate adoption and value-based outcomes

Partner with data engineering team on:

• Data product and semantic layer alignment

• AI orchestration

• Observability and feedback loops

Enable internal teams on:

• Agent design patterns

• Responsible AI practices

• Ongoing support and scaling

Technical Expertise:

Strong experience with:

• Azure AI services (Foundry, Cognitive Services, AI Search)

• Microsoft Copilot Studio / Power Platform

• Cloud-native architecture (Azure)

Experience building:

• Conversational AI / chatbot / agent solutions

• RAG pipelines and LLM-based applications

• API integrations, MCP frameworks, and enterprise workflows

Architecture & Engineering:

Proven ability to design:

• Scalable, secure AI platforms

• Hybrid architectures (low-code + pro-code)

Experience with:

• MLOps and CI/CD pipelines

• Monitoring and observability (App Insights, logging, tracing)

• Secure cloud networking and identity

• Validating and optimizing AI systems

• Evaluating and selecting AI platforms and tools (cloud-native and third-party)

• Defining design patterns, standards, and guardrails for AI solutions

• Balancing rapid experimentation with maintaining platform consistency and avoiding fragmentation

Governance & Responsible AI:

Experience implementing:

• AI governance frameworks

• Data security, privacy and compliance controls

• Lifecycle management and deployment gating

Understanding of regulatory considerations (e.g., data privacy, AI compliance risks)

• Experience with auditing, monitoring, and incident response for AI systems in production

Experience:

• 5+ years in cloud / data / AI engineering or architecture roles

• 5+ years in software development or data engineering

• Hands-on experience building LLM-based applications or agentic AI solutions

Preferred Qualifications:

Microsoft certifications (Azure AI Engineer, Azure Architect, etc.)

Experience integrating AI agents with:

• Microsoft Teams / M365

• ERP / CRM systems (SAP, Salesforce)

Familiarity with Databricks or lakehouse architectures

Experience delivering multi-phase AI programs (pilot scale)

Engagement Model:

• First 90 days: co-build solutions with Microsoft Partner

• Establish AI foundation and standards

• Drive adoption and value realization

• Create support model and roadmap enterprise AI solutions

What Success Looks Like:

• Production-ready AI agents deployed within Microsoft 365

• Scalable AI agent platform established (governed, monitored, secure)

• Internal teams enabled to independently build and operate agents

• Clear pipeline of business-driven AI use cases

• Consistent use of defined AI platforms and patterns across teams

• Reduced duplication and fragmentation across AI solutions

• Clear intake, prioritization, and decision-making for AI use cases

• Align with vendor best practice to build, take ownership, and continue building value for the organization!