This role seeks a seasoned GENAI and Agentic AI Architect with extensive practical experience in Microsoft Azure AI services, multi-agent frameworks, LLM orchestration, RAG, Python, and cloud-native engineering.

The primary focus is on designing secure, scalable, and production-ready GenAI solutions. This includes establishing reference architectures, leading complex implementations, and promoting the enterprise-wide adoption of GenAI and Agentic AI capabilities.

Architecting comprehensive GenAI and Agentic AI solutions utilizing Azure OpenAI, Azure AI Foundry, Azure Agent Service, Microsoft Agent Framework, Azure Machine Learning, LangGraph, and CrewAI.

Designing sophisticated multi-agent systems that encompass planning, reasoning, memory management, state handling, tool invocation, human oversight, and intricate workflow orchestration.

Developing enterprise RAG solutions leveraging Azure AI Search, vector search, Cosmos DB, PostgreSQL pgvector, and Redis.

Creating robust prompt pipelines, embedding workflows, advanced retrieval strategies, context enrichment mechanisms, seamless tool integrations, effective model evaluation processes, and essential AI guardrails.

Building Python-based microservices with FastAPI or Django and integrating them with Azure AI and existing enterprise applications.

Establishing LLMOps and deployment best practices using Azure Machine Learning, Azure DevOps, GitHub Actions, AKS, Azure Functions, Azure Monitor, Application Insights, and Log Analytics.

Defining stringent architecture standards for security, reliability, scalability, observability, performance, content safety, and cost optimization.

Leading architecture reviews, conducting threat modeling, performing performance benchmarking, developing technical prototypes, and ensuring production readiness.

A minimum of 15 years of professional technology experience, demonstrating strong Azure architecture and engineering expertise.

Proven experience with GenAI, Agentic AI, Azure OpenAI, Azure AI Foundry, Azure Agent Service, Azure Machine Learning, LangGraph, and CrewAI.

Deep expertise in RAG, embeddings, vector databases, multi-agent orchestration, LLMOps, and model evaluation techniques.

Proficiency in Python, utilizing frameworks like FastAPI or Django.

Hands-on experience with Azure AI Search, Cosmos DB, AKS, Azure Functions, API Management, Event Grid, and Service Bus.

Solid understanding of Azure security principles, distributed systems, scalability, performance optimization, and cost management.

An Azure Solutions Architect certification is a plus.