About the role
Agentic AI MCP Engineer Charlotte, NC ( Need Onsite day 1, hybrid 3 da ys from office) Job Description We are seeking an experienced Agentic AI MCP Engineer to design, develop, and deploy intelligent AI agents using the Model Context Protocol (MCP)
The Role Responsibilities
- Design, develop, and deploy Agentic AI solutions leveraging Model Context Protocol (MCP) architecture.
- Build and manage MCP servers, tools, resources, prompts, and enterprise integrations.
- Develop multi-agent workflows and autonomous AI systems capable of planning, reasoning, and executing business processes.
- Integrate Large Language Models (LLMs) with enterprise applications, APIs, databases, and third-party platforms.
- Implement Retrieval-Augmented Generation (RAG), vector search, and knowledge retrieval capabilities.
- Develop and optimize AI agents using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or Semantic Kernel.
- Create secure, scalable, and production-ready AI solutions following enterprise governance standards.
- Collaborate with business stakeholders, architects, data engineers, and product teams to deliver AI-driven solutions.
- Monitor, evaluate, and improve agent performance, response quality, and operational efficiency.
- Support AI observability, security, compliance, and responsible AI initiatives across enterprise environments.
Requirements
- 5+ years of software engineering experience with at least 2+ years focused on Generative AI and Agentic AI development.
- Strong expertise in Python programming and API development.
- Hands-on experience with Model Context Protocol (MCP) implementations and integrations.
- Experience working with AI orchestration frameworks including LangChain, LangGraph, CrewAI, AutoGen, or Semantic Kernel.
- Strong knowledge of Large Language Models (LLMs) such as OpenAI GPT, Azure OpenAI, Anthropic Claude, Gemini, Llama, or Mistral.
- Experience building Retrieval-Augmented Generation (RAG) solutions and working with vector databases.
- Knowledge of embeddings, semantic search, prompt engineering, and AI evaluation techniques.
- Experience integrating AI solutions with enterprise systems, REST APIs, cloud services, and databases.
- Strong understanding of AI governance, security, monitoring, and compliance best practices.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP).
- Excellent communication and collaboration skills with the ability to work across cross-functional teams.
Preferred, But Not Required
- Experience building enterprise-scale multi-agent ecosystems.
- Knowledge of AI workflow orchestration, event-driven architectures, and agent lifecycle management.
- Experience with vector databases such as Pinecone, Weaviate, Chroma, FAISS, or Milvus.
- Exposure to MLOps, AI observability, and production monitoring frameworks.
- Financial Services, Banking, or Capital Markets domain experience.
- Relevant certifications in Artificial Intelligence, Cloud Platforms, or Data Engineering.
- Experience working within Agile/Scrum development environments.
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