Build and Deploy an MCP-Powered AI Workflow Using FastAPI and AWS

Worldwide | Aug. 6, 2026

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Company: Upwork

Country: Worldwide

Type: Remote

Employment: Contract

Description: We are looking for a backend engineer to build and deploy a small AI workflow using the Model Context Protocol (MCP). Workflow The application should allow a user to ask a question about business data stored in an existing Flask application. The MCP workflow will: 1. Receive a user request through a FastAPI endpoint. 2. Allow an LLM to call an MCP tool. 3. Retrieve the required data from an existing Flask API. 4. Return a clear, structured response to the user. 5. Log the request, tool execution, response status, and failures. Example request: “Show me the customers who require follow-up this week.” The MCP tool should securely call the Flask API, retrieve the relevant records, and provide the results to the LLM. Technical Scope • Build a FastAPI service that acts as the AI and MCP client layer. • Create one custom MCP server or MCP tool. • Integrate the MCP tool with an existing Flask REST API. • Connect the workflow to OpenAI, Anthropic, or another supported LLM. • Add authentication between FastAPI and Flask. • Add input validation, timeouts, retries, and error handling. • Dockerize the services. • Deploy the application to AWS. • Add basic logging and monitoring. Preferred AWS Deployment The solution may use: • AWS ECS Fargate or EC2 • Amazon ECR • Application Load Balancer • AWS Secrets Manager • Amazon CloudWatch • PostgreSQL or an existing business database Deliverables • FastAPI application • Custom MCP server or tool • Flask API integration • LLM tool-calling workflow • Dockerfile and Docker Compose configuration • AWS deployment configuration • Environment and secrets configuration • Logging and error handling • Unit and integration tests • README with local setup and deployment instructions • Short recorded or live demonstration Required Experience • Strong Python experience • FastAPI and Flask • REST API development • AWS deployment • Docker • LLM tool calling • MCP server or client development • Authentication and secure credential handling Acceptance Criteria The project will be considered complete when: • A user can submit a request through the FastAPI endpoint. • The LLM correctly selects and invokes the MCP tool. • The MCP tool retrieves data from the Flask API. • The final response accurately reflects the returned business data. • Authentication, failure handling, and logging work correctly. • The application can be deployed and tested successfully on AWS. Please include examples of similar FastAPI, Flask, AWS, MCP, or LLM integration work in your proposal.

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