Agentic AI MCP Engineer

Job Summary We are seeking an experienced Agentic AI MCP Engineer to design, develop, and deploy intelligent AI agents using the Model Context Protocol (MCP). The ideal candidate will have strong expertise in Generative AI, Agentic AI, Python, MCP architecture, AI orchestration frameworks, LLMs, RAG, vector databases, and enterprise integrations.

Key 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.

Required Qualifications 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 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 Qualifications 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.

Education: Bachelors Degree