Azure AI Architect

Role & responsibilities

• Define targetstate Azure AI reference architectures using Azure AI Foundry, Azure OpenAI, and Azure AI Document Intelligence for intelligent document processing, enterprise copilots, and knowledgeretrieval applications.
• Establish standards for RAG architectures: chunking, embeddings, vector indexing, hybrid ranking, grounding, and citation-based policies using Azure Cognitive Search or vector-enabled data stores.
• Create architectural decision records (ADRs), enterprise guardrails, and reusable blueprints to accelerate solution delivery.
• Lead full-stack AI solution design including integration patterns (Azure Functions, APIM, Logic Apps, Event Grid), API contracts, data flow, security boundaries, and observability.
• Architect document intelligence workflows for classification, extraction, OCR, validation, and seamless integration with CRM/ERP/ITSM/EDM platforms.
• Define and ensure adherence to non-functional requirementsavailability, latency, scalability, throughput, DR/RTO/RPO, and cost.
• Implement enterprise-grade security: Entra ID, RBAC, network isolation (Private Endpoints), Key Vault secrets, data encryption, governance, and content filtering.
• Embed Responsible AI practices including safety filters, content controls, prompt and data governance, redteaming, and humanintheloop flows.
• Ensure compliance with regulatory standards (GDPR, ISO 27001, legal, risk, and security assessments).
• Define MLOps and LLMOps processes for model versioning, evaluation, deployment, rollback, drift monitoring, and continuous improvement.
• Instrument observability across AI workloads (logging, tracing, metrics, App Insights) and define SLIs/SLOs for mission-critical AI services.
• Drive FinOps discipline—token optimization, model selection, caching, throughput control, and cost monitoring.
• Mentor developers and engineers on prompt engineering, grounding techniques, tool-use patterns, evaluation frameworks, and best practices for LLM design.