AI Architect – Enterprise LLM and Power Platform

About the role

The Cross Stream Innovation Architect leads architecture and technical strategy across AI product delivery, innovative software engineering, and citizen development enablement, ensuring secure, scalable, and measurable solutions.

Key responsibilities

• Define and communicate architecture vision and standards for AI, applications, data, integration, and security across all streams.
• Develop and maintain an enterprise LLM playbook, including model selection, usage guidelines, cost monitoring, and governance.
• Author reference blueprints for Conversational AI, agentic orchestration, and Retrieval‑Augmented Generation pipelines.
• Guide solution design from business needs to end‑to‑end integration, data contracts, and release strategy.
• Architect Power Platform core (environments, DLP, connectors, ALM, identity & access) and co‑design the Center of Excellence operating model for citizen development.
• Implement Responsible AI guardrails, incident playbooks, observability, and SLOs.
• Mentor engineers and makers, promote framework‑first delivery, and present architecture to leadership.

Required profile

• 5–7+ years in enterprise/solution/AI architecture with hands‑on delivery of production AI or automation systems.
• Deep experience with Azure AI, Azure OpenAI, Copilot Studio, RAG pipelines, vector stores, and prompt engineering.
• Strong background in cloud‑native Azure architecture, APIs, events, security, and integration patterns.
• Proven governance of Power Platform (Dataverse, DLP, environment strategy, ALM) and scaling citizen development.
• Demonstrated implementation of Responsible AI controls, bias/fairness checks, and red/blue‑team processes.

Required skills

• Azure AI / Azure OpenAI
• Copilot Studio
• RAG pipelines and vector stores
• Prompt engineering and evaluation harnesses
• Cloud‑native Azure architecture
• API design, event‑driven integration
• Security and data governance
• Power Platform (Dataverse, DLP, ALM)
• Responsible AI practices