Build AI Agent & Workflow Automation SaaS with Python, Claude & LangChain

We are looking for an experienced AI Engineer to build an AI Agent Automation SaaS product for business workflows. This is not a basic chatbot project. We need someone who can build a production-ready AI system that automates manual work, connects with business tools, uses company knowledge, and provides a secure dashboard for managing workflows. The ideal engineer should be able to own the architecture from AI Agent logic and backend APIs to integrations, dashboard workflows, authentication, database design, and deployment. --- PROJECT SCOPE --- 1. AI Agent and Automation System Build AI Agents that can automate repetitive business tasks such as lead qualification, customer support, data extraction, document processing, reporting, operations, and internal workflows. The system should support multi-step AI workflows, tool calling, API actions, structured outputs, retries, fallback logic, and human approval steps where needed. 2. Claude and OpenAI Integration Integrate Claude and OpenAI APIs for intelligent task execution, prompt workflows, function calling, model routing, structured outputs, and reliable business automation. The system should use the right model and workflow based on task type, cost, speed, and response quality. 3. LangChain and Agent Orchestration Use LangChain for AI Agent workflows, tool integrations, memory, document retrieval, and structured agent logic. Use LangGraph where multi-step or stateful agent orchestration is needed. Experience with CrewAI, AutoGen, or MCP is helpful for advanced multi-agent workflows but is not mandatory. 4. RAG Knowledge System Build a Retrieval Augmented Generation system so AI Agents can retrieve accurate information from internal documents, websites, uploaded files, and business data. The RAG system should use embeddings, PostgreSQL, pgvector, vector search, and grounded answers to reduce hallucinations. 5. Python Backend and SaaS Architecture Build a secure Python backend using FastAPI and REST APIs. The platform should support authentication, role-based access, organizations or workspaces, workflow triggers, audit-friendly data handling, and scalable SaaS-ready architecture. 6. Integrations and Workflow Automation Connect the platform with external APIs, webhooks, CRMs, email tools, calendars, databases, and other business systems. Use n8n or direct API integrations where appropriate to automate business workflows end to end. 7. Dashboard and Admin Experience Build or improve a clean dashboard for users to manage AI Agents, workflows, documents, knowledge sources, integrations, and automation results. Frontend work may include React, Next.js, and TypeScript depending on the product architecture. 8. Infrastructure, Documentation, and Handoff Use PostgreSQL, Supabase where suitable, Docker, GitHub, and cloud deployment tools for a reliable production setup. Provide clean documentation covering system architecture, API endpoints, agent workflows, environment setup, deployment steps, and maintenance guidance. --- REQUIRED EXPERIENCE --- • Strong Python and FastAPI experience • Proven AI Agent Development and AI Automation experience • Experience with Claude API, OpenAI API, LangChain, and LLM workflow design • Experience with RAG, embeddings, vector databases, PostgreSQL, and pgvector • Experience with API Integration, REST APIs, webhooks, and external business tools • Experience building SaaS products, dashboards, authentication, and role-based access • Strong problem-solving, architecture, documentation, and production delivery skills --- NICE TO HAVE --- • LangGraph for stateful or multi-step agent workflows • CrewAI, AutoGen, MCP, or Multi-Agent Systems • n8n workflow automation • Supabase, Docker, AWS, Vercel, or Stripe • Voice AI, Twilio, ElevenLabs, Vapi, Retell, or AI Receptionist systems • React, Next.js, and TypeScript --- DELIVERABLES --- • Production-ready AI Agent Automation system • Python/FastAPI backend with documented APIs • Claude and OpenAI integration with tool calling and structured outputs • LangChain-based AI Agent workflows • RAG knowledge system with PostgreSQL, pgvector, and embeddings • Secure SaaS-ready architecture with authentication and role-based access • External API, webhook, and workflow automation integrations • Dashboard or admin interface for managing workflows and knowledge sources • Docker deployment setup, clean GitHub repository, and full technical documentation --- IDEAL CANDIDATE --- Please share examples of AI Agents, AI Automation workflows, RAG systems, LangChain applications, or AI SaaS products you have built. In your proposal, explain: 1. Your role in the project 2. The architecture and stack you used 3. How you handled AI Agent logic, RAG, APIs, and deployment 4. A relevant production outcome or result