AI Engineer – Agentic AI / Generative AI

Job Description - AI Engineer Job Title: AI Engineer - Agentic AI / Generative AILocation: Louisville, KYDuration: 6 monthsExperience Required: 8-10 yearsRole: AI EngineerEssential Skill: AI EngineeringPrimary Focus: Agentic AI, RAG, MCP, Multi-Agent Orchestration, Python, LangChain, LangSmith, and Graph DatabasesDesirable Skill: Google Cloud Platform (GCP)Keyword: AI and Automation Must-Have Technical Skills Strong experience in Agentic AI Full Stack development. Hands-on experience with Retrieval-Augmented Generation (RAG). Experience with Model Context Protocol (MCP). Strong experience in Multi-Agent orchestration. Experience with end-to-end AI solution deployment. Strong hands-on experience with LangChain. Experience with LangSmith. Strong Python programming skills. Experience with Graph Databases, such as:Neo4jOther graph database technologiesExperience building scalable AI/GenAI applications from development through production deployment. Good-to-Have Skills Google Cloud Platform (GCP) experience. Generative AI application development. AI/ML platform and cloud deployment experience. Experience with enterprise AI and automation solutions. Roles & Responsibilities Design and develop Agentic AI full-stack solutions. Build production-ready RAG-based applications. Design and implement multi-agent architectures and orchestration frameworks. Implement MCP-based integrations for AI agents and tools. Develop AI applications using Python. Build conversational and intelligent applications using LangChain. Use LangSmith for LLM application development, tracing, evaluation, and monitoring. Design and implement graph-based solutions using Neo4j or similar graph databases. Develop AI solutions that integrate LLMs, enterprise data, APIs, tools, and external systems. Manage end-to-end deployment of AI applications from development through production. Design scalable and reliable AI application architectures. Integrate RAG pipelines with enterprise knowledge sources and data repositories. Develop agent workflows capable of reasoning, tool usage, task execution, and multi-agent collaboration. Troubleshoot and optimize AI application performance, reliability, and scalability. Collaborate with engineering and business teams to translate requirements into AI-driven solutions. Follow software engineering, testing, deployment, and monitoring best practices. Leverage GCP for AI application development and deployment where applicable. Key Skills / Keywords Agentic AIGenerative AIAI EngineeringAI AutomationRAGRetrieval-Augmented GenerationMCPModel Context ProtocolMulti-Agent SystemsMulti-Agent OrchestrationLangChainLangSmithPythonNeo4jGraph DatabaseLLMLLM Application DevelopmentAI Full StackEnd-to-End DeploymentGCPGoogle CloudAI ArchitectureAI/ML Integration