AI-Driven Test Generation Workflow Deployment

We’re developing an AI-driven workflow that automates test generation and execution using LLMs, Retrieval-Augmented Generation (RAG), and intelligent test-data management.
The goal of this short-term engagement is to deploy a secure, lightweight cloud pilot that runs the full workflow end-to-end.

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Responsibilities
• Deploy containerized infrastructure using Docker and AWS ECS / EC2
• Configure and manage databases (PostgreSQL / MongoDB / Redis) for test-data handling
• Set up S3 / MinIO object storage for datasets and assets
• Integrate the RAG knowledge base with the test-data system using Python API endpoints
• Implement security controls: TLS 1.3, AES-256, IAM roles, MFA, and audit logging
• (Optional) Create a lightweight CI/CD pipeline (GitHub Actions or Bitbucket)
• Provide clear documentation for environment setup, architecture, and handover

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Deliverables
• Secure, deployed cloud environment (containers + data layers)
• Working RAG-to-Test-Data API integration
• Verified encryption, IAM, and logging setup
• Deployment & configuration documentation
• (Optional) CI/CD workflow for redeployments

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Required Skills
• Cloud platforms: AWS (ECS, ECR, IAM, S3, EC2, CloudWatch)
• Containerization: Docker, environment orchestration
• Databases: PostgreSQL / MongoDB / Redis
• Backend integration: Python (FastAPI / Flask / Lambda)
• Network & API security: TLS, encryption, IAM, MFA
• CI/CD tools: GitHub Actions / Bitbucket Pipelines

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Nice-to-Have
• Experience with LLM or RAG systems (LangChain, LlamaIndex, OpenAI API)
• Familiarity with synthetic data generation or data masking
• Understanding of GDPR and data protection principles (UK/EU context)
• Exposure to test automation frameworks like Playwright