> Markdown version of [/jobs/ext/4569891-ai-agentic-tester](https://www.wearedevelopers.com/jobs/ext/4569891-ai-agentic-tester). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Agentic Tester - **Company:** Artech - **Location:** Thornton, CO, United States - **Experience:** Experienced - **Salary:** $83,200.0 - $87,360.0 - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Python (Programming Language), OpenAI, Swagger, Selenium, Strategies of Testing, Postman, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Agentic-AI, Pytest, AI Platforms, Playwright, Virtual Agents, Google Gemini, Restful APIs, Api Management - **Published:** November 4, 2026 - **Apply:** https://www.thejobnetwork.com/job/69916ff9-5c0f-4b8d-a2ea-160bbc266629/ai-agentic-tester ## About the Role Skills: QA/Test Engineering experience Must have- Experience testing Generative AI, LLM, or AI-based applications., 3-8 years of QA/Test Engineering experience. Experience testing Generative AI, LLM, or AI-based applications. Knowledge of AI Agents, Agentic AI, RAG, and prompt engineering. Hands-on experience with API testing (Postman, Swagger, REST APIs). Experience with automation frameworks (Selenium, Playwright, PyTest). Proficiency in Python. Understanding of AI evaluation metrics and testing methodologies Knowledge of Azure OpenAI, AWS Bedrock, Gemini, or similar AI platforms Appreciate your quick response and please feel free to reach me out for any query you may have. ## Description Validate AI agent workflows against business requirements. Test end-to-end AI use cases, including multi-agent interactions. Verify task completion accuracy and output quality. Test agent orchestration, tool calling, and workflow execution. AI Model Validation Evaluate LLM responses for accuracy, relevance, and consistency. Test prompt engineering effectiveness and response quality. Validate Retrieval-Augmented Generation (RAG) outputs. Measure hallucination rates and response reliability.