AI Engineer - Agentic AI / Generative AI

Location: Louisville, KY

Duration: 6 months

Experience Required: 8-10 years Primary Focus: Agentic AI, RAG, MCP, Multi-Agent Orchestration, Python, LangChain, LangSmith, and Graph Databases Keyword: AI and Automation

Job Description: AI Engineer

Job Title: AI Engineer - Agentic AI / Generative AI

Location: Louisville, KY

Duration: 6 months

Experience Required: 8-10 years

Role: AI Engineer Essential Skill: AI Engineering Primary Focus: Agentic AI, RAG, MCP, Multi-Agent Orchestration, Python, LangChain, LangSmith, and Graph Databases Desirable 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: Neo4j Other graph database technologies Experience 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 AI Generative AI AI Engineering AI Automation RAG Retrieval-Augmented Generation MCP Model Context Protocol Multi-Agent Systems Multi-Agent Orchestration LangChain LangSmith Python Neo4j Graph Database LLM LLM Application Development AI Full Stack End-to-End Deployment GCP Google Cloud AI Architecture AI/ML Integration