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We are seeking a forward-thinking Agentic AI Developer to design, build, and scale autonomous AI agents and multi-agent systems. Unlike traditional automation, you won't just be connecting APIs; you will be building "brains" within n8n that can reason, plan, and execute complex business logic with minimal human intervention.
Job Responsibility:
Agentic Workflow Design: Architect end-to-end autonomous workflows in n8n, utilizing the AI Agent node, specialized sub-workflows, and the Model Context Protocol (MCP)
Tool Integration: Develop and maintain custom "tools" (via HTTP requests or JavaScript/Python nodes) that allow AI agents to interact with our CRM, databases, and third-party SaaS platforms
Multi-Agent Orchestration: Build systems where multiple specialized agents (e.g., a "Researcher Agent" and a "Writer Agent") collaborate to complete high-level objectives
Prompt Engineering: Design and refine system prompts using techniques like Chain-of-Thought (CoT) and ReAct to ensure reliable and logical agent behavior
Human-in-the-Loop (HITL): Implement "Wait for Webhook" or approval nodes to ensure critical AI decisions are reviewed by humans before execution
Retrieval-Augmented Generation (RAG): Integrate vector databases (e.g., Pinecone, Qdrant) within n8n to provide agents with long-term memory and proprietary knowledge
Monitoring & Debugging: Use n8n’s execution logs and external tools to monitor agent "hallucinations," rate limits, and workflow failures
Requirements:
Technical Mastery of n8n: Deep experience with n8n’s Advanced AI nodes (Agent, Chain, Memory, and Tool nodes)
Ability to write complex JavaScript expressions and use the Code Node for custom data manipulation
Expertise in JSON data structures—specifically how n8n passes items between nodes
AI & LLM Literacy: Hands-on experience with LLM providers (OpenAI, Anthropic, Google Gemini) and local models (Ollama/Llama)
Understanding of Agent Frameworks (e.g., how to translate LangChain or CrewAI concepts into n8n workflows)
Knowledge of Function Calling and how to describe tools so an LLM can use them effectively
Backend & DevOps: Proficiency in API integration (REST, Webhooks, OAuth2 authentication)
Familiarity with Docker for self-hosting n8n and managing environment variables
Basic database knowledge (SQL/NoSQL) for logging agent states and maintaining persistent memory
Nice to have:
Experience with Python for advanced data processing within n8n
Knowledge of Model Context Protocol (MCP) to standardize how agents access local and remote resources
A portfolio of "Agentic" projects (e.g., an autonomous lead research agent or a self-correcting customer support bot)