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Azure AI Architect
Role Overview
The Azure AI Architect is responsible for end‑to‑end AI solution architecture across Azure services, ensuring enterprise‑grade scalability, security, governance, and integration with downstream platforms such as Microsoft Power Platform.
Key Responsibilities
- Own AI and GenAI architecture across Azure services including:
- Azure OpenAI
- Azure AI Services
- Azure Machine Learning
- Azure Cognitive Search
- Azure Data Services (Synapse, Fabric, Cosmos DB, Data Lake)
- Define reference architectures for:
- Agentic AI and Copilot‑style experiences
- RAG (Retrieval‑Augmented Generation)
- Intelligent document processing
- AI‑driven workflow orchestration
- Make explicit architectural decisions on:
- What belongs in Azure vs what (optionally) surfaces through Power Platform
- When Power Platform is an accelerator vs a liability
- Define integration patterns between Azure AI services and Power Platform using:
- APIs, Azure Functions, Logic Apps
- Asynchronous and event‑driven patterns
- Establish governance standards:
- Model lifecycle, prompt/version control
- Data security, PII handling, RBAC
- Cost management and quota controls
- Review and challenge solution designs proposed by engineering and low‑code teams
- Provide architectural oversight for CI/CD, DevSecOps, and environment strategies
The Architect must actively prevent misuse of Power Platform where it introduces:
- Performance bottlenecks
- Hidden licensing cost explosions
- Maintainability risks
Required Skills & Experience
- 10+ years in solution or cloud architecture
- Strong hands‑on experience with Azure AI & GenAI services
- Proven experience designing enterprise‑scale AI systems
- Strong understanding of Power Platform capabilities and constraints, not just features
- Experience working with platform governance, CoE models, and controlled low‑code adoption
- Azure certifications (Architect / AI Engineer) preferred
Azure AI Engineer
Role Overview
The Azure AI Engineer is responsible for building, deploying, and optimizing AI solutions designed by the Azure AI Architect. This role is hands‑on and execution‑focused, with a working—but not architectural—understanding of the Power Platform for integration and consumption scenarios.
Key Responsibilities
- Implement AI solutions using:
- Azure OpenAI
- Azure Machine Learning
- Azure AI Services
- Vector stores and embeddings
- Build and manage:
- Model inference pipelines
- Prompt engineering and evaluation frameworks
- RAG pipelines (chunking, indexing, retrieval)
- Develop backend services using:
- Azure Functions, App Services, Containers
- Python, .NET, or similar languages
- Expose AI capabilities via:
- Secure REST APIs
- Event‑driven integrations
- Integrate AI services with Power Platform where required:
- Custom connectors
- API‑based invocation from Power Apps, Power Automate, Copilot Studio
- Implement logging, monitoring, and telemetry for AI workloads
- Support CI/CD pipelines for AI code and configurations
The Engineer should not:
- Build core AI logic inside Power Automate
- Design agent reasoning directly in Copilot Studio
- Treat Power Platform as a backend replacement
Required Skills & Experience
- 4–8 years of software or cloud engineering experience
- Strong coding skills in Python / .NET / Java
- Hands‑on experience with Azure AI & GenAI services
- Experience deploying and operating AI workloads in production
- Working knowledge of Power Platform integration patterns
- Azure AI Engineer Associate certification preferred
