What are the responsibilities and job description for the AI Architect (Azure AI Foundry) | W2 Only | Remote | position at Xlysi?
Design and implement Agentic AI solutions using Microsoft Azure AI Foundry for enterprise-scale deployments.
Architect Multi-Agent Decision Frameworks to orchestrate autonomous agent collaboration and goal resolution.
Engineer fault-tolerant agent systems with end-to-end observability, monitoring, and self-healing capabilities.
Evaluate and deploy platforms for agent creation, lifecycle management, and production deployment.
Design workflow management systems governing agent and node interactions, sequencing, and state management.
Architect Meta-Agent (Agents of Agents) hierarchies for complex, layered autonomous decision-making.
Implement A2A (Agent-to-Agent) Protocol standards for secure, structured inter-agent communication.
Assess and select agentic frameworks (LangGraph, LangChain, AutoGen, Semantic Kernel) based on use-case fit.
Govern MCP Hub architecture, defining policies and standards across a centralized pool of MCP Servers.
Define MCP Server boundaries, responsibilities, and segregation strategies within the enterprise hub.
Advise decision criteria for MCP vs. Azure APIM based on integration patterns and governance needs.
Evaluate and select OCR solutions (Azure Document Intelligence vs. John Snow Labs) aligned to accuracy and scale requirements.
Design OCR pre-processing pipelines (de-noise, de-skew, binarization) and post-processing validation workflows.
Architect and recommend between RAG (Retrieval-Augmented Generation) and CAG (Cache-Augmented Generation) based on latency, cost, and freshness trade-offs.
Architect Multi-Agent Decision Frameworks to orchestrate autonomous agent collaboration and goal resolution.
Engineer fault-tolerant agent systems with end-to-end observability, monitoring, and self-healing capabilities.
Evaluate and deploy platforms for agent creation, lifecycle management, and production deployment.
Design workflow management systems governing agent and node interactions, sequencing, and state management.
Architect Meta-Agent (Agents of Agents) hierarchies for complex, layered autonomous decision-making.
Implement A2A (Agent-to-Agent) Protocol standards for secure, structured inter-agent communication.
Assess and select agentic frameworks (LangGraph, LangChain, AutoGen, Semantic Kernel) based on use-case fit.
Govern MCP Hub architecture, defining policies and standards across a centralized pool of MCP Servers.
Define MCP Server boundaries, responsibilities, and segregation strategies within the enterprise hub.
Advise decision criteria for MCP vs. Azure APIM based on integration patterns and governance needs.
Evaluate and select OCR solutions (Azure Document Intelligence vs. John Snow Labs) aligned to accuracy and scale requirements.
Design OCR pre-processing pipelines (de-noise, de-skew, binarization) and post-processing validation workflows.
Architect and recommend between RAG (Retrieval-Augmented Generation) and CAG (Cache-Augmented Generation) based on latency, cost, and freshness trade-offs.