QA Engineer for AI Products/Solutions
About The Position
TekWissen is a global workforce management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients worldwide. Our client is a leading global technology consulting and digital transformation organization that helps enterprises modernize their business operations through innovative cloud, data, and customer engagement solutions. With a strong focus on delivering scalable technology platforms and customer-centric digital experiences, the organization empowers businesses to drive growth, improve operational efficiency, and enhance customer relationships.
Requirements
- Hands-on experience testing LLM-based products (chatbots, copilots, RAG systems, AI agents) — designing test cases for non-deterministic, generative outputs
- Practical experience with AI/LLM evaluation frameworks (e.g., Ragas, DeepEval, LangSmith, Promptfoo, OpenAI Evals, TruLens) — building eval suites, scoring rubrics, and golden datasets
- Working knowledge of eval metrics for generative AI: hallucination rate, faithfulness/groundedness, relevance, answer correctness, toxicity/bias scoring, BLEU/ROUGE/semantic similarity where applicable
- Experience with prompt regression testing — validating prompt changes and model/version upgrades against baseline eval sets
- Strong proficiency in Python for writing eval scripts, test harnesses, and data validation logic
- Familiarity with SQL and data validation techniques
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 (data quality, schema, drift detection)
- Collaborate with data scientists/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
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What This Job Offers
Career Level
Mid Level
Education Level
No Education Listed
