QA Engineer for AI Products

This is a 6-month contract position for a QA Engineer specializing in AI products, based in Bellevue, Washington. The role is hybrid and requires local candidates for in-person collaboration.

Responsibilities

• Design and execute test plans for AI/ML-driven features, including model outputs, prompts, and integrated application behavior
• Build and maintain automated test suites covering functional, regression, integration, and API testing
• Evaluate model outputs for accuracy, consistency, bias, hallucination, and edge-case failures
• Develop evaluation frameworks and golden datasets/test cases to benchmark model performance over time
• Test prompt engineering changes, model version upgrades, and fine-tuning outputs for regressions
• Perform adversarial and red-team style testing to surface safety, security, and robustness issues
• Validate data pipelines feeding into AI models, including data quality, schema, and drift detection
• Collaborate with data scientists and ML engineers to define acceptance criteria and quality metrics for models
• Test latency, scalability, and reliability of AI services under load
• Contribute to CI/CD pipelines, integrating automated and model-evaluation tests
• Design test cases for LLM-based products such as chatbots, copilots, RAG systems, and AI agents, accounting for non-deterministic and generative outputs
• Build evaluation suites, scoring rubrics, and golden datasets using AI/LLM evaluation frameworks
• Validate prompt changes and model/version upgrades against baseline evaluation sets
• Write evaluation scripts, test harnesses, and data validation logic

Qualifications

• Required

• 5+ years of QA and testing experience

• Hands-on experience testing LLM and generative AI products

• Practical experience with AI/LLM evaluation frameworks (such as Ragas, DeepEval, LangSmith, Promptfoo, OpenAI Evals, or TruLens)

• Working knowledge of evaluation metrics for generative AI, including hallucination rate, faithfulness/groundedness, relevance, answer correctness, toxicity/bias scoring, and semantic similarity measures

• Strong proficiency in Python for writing test scripts and validation logic

• Experience with SQL and data validation techniques

• Test automation expertise

• Preferred

• Prompt engineering or prompt testing experience

• CI/CD pipeline automation experience

• Exposure to Azure, Snowflake, or cloud data platforms