AgenticAI MCP Engineer || Must have MCP experience || Charlotte, NC(Onsite/Hybrid)

Position: AgenticAI MCP Engineer

Location: Charlotte, NC(Hybrid/Onsite)

Duration: Contract

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 (Google Cloud Platform).
• 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.