AI Solutions Architect (MCP & Agent-to-Agent Integration)

Key Responsibilities

• Design enterprise AI solutions using LLMs, AI agents, and multi-agent systems.
• Architect AI applications leveraging Model Context Protocol (MCP) for standardized tool and data integration.
• Design and implement Agent-to-Agent (A2A) communication patterns for collaborative AI workflows.
• Build scalable Retrieval-Augmented Generation (RAG) architectures.
• Define AI solution architecture, security, governance, and deployment strategies.
• Integrate AI applications with enterprise systems, APIs, databases, and SaaS platforms.
• Design agent orchestration workflows using frameworks such as LangGraph, CrewAI, AutoGen, or Semantic Kernel.
• Develop AI-powered automation for enterprise business processes.
• Collaborate with data engineering teams to build vector search and knowledge retrieval pipelines.
• Establish best practices for prompt engineering, evaluation, observability, and model lifecycle management.
• Optimize AI applications for latency, scalability, reliability, and cost.
• Mentor engineering teams on AI architecture and implementation standards.
• Stay current with advancements in LLMs, agentic AI, MCP, and enterprise AI platforms.