An AI Test Engineer (or AI/ML Quality Assurance Engineer) blends traditional software testing with specialized machine learning and Large Language Model (LLM) evaluation to ensure AI systems are accurate, safe, and reliable. [1]

Key Responsibilities

- AI/ML Model &
- Output Validation:

Test AI-generated content, predictions, and responses for factual accuracy, consistency, and alignment.
- Hallucination &
- Bias Detection: Design evaluation suites to catch model hallucinations, toxic or harmful outputs, and demographic or data bias.
- Prompt &
- Regression Testing: Automate prompt-based test suites and track model regression across different prompt or model versions.
- API &
- Data Pipeline Validation: Test REST/GraphQL APIs, vector databases, and the data pipelines feeding the AI models.
- Safety &
- Guardrail Compliance: Check applications against security risks (prompt injection, PII/data leakage) and compliance frameworks (e.g., SOC 2, ISO 27001, NIST AI RMF).
- CI/CD Integration:



Embed automated AI evaluation metrics and quality gates directly into deployment pipelines.

Required Skills &
- Qualifications

- Programming &
- Scripting:

Robust proficiency in Python (most common for AI/ML testing), Java, or TypeScript/JavaScript.
- Testing Frameworks: Experience with UI/functional automation tools like Playwright, Selenium, or Cypress, alongside test frameworks like PyTest.
- AI Evaluation Tools: Familiarity with specialized LLM evaluation and observability tools such as LangSmith, Ragas, DeepEval, or model platforms.
- API &
- Data Testing: Hands-on experience with Postman, Swagger, SQL, and data validation (Pandas/NumPy).
- Core Concepts: Understanding of machine learning metrics, train/test splits, model drift, and Retrieval-Augmented Generation (RAG) architectures.
- Education: A Bachelor's or Master’s degree in Computer Science, Information Technology, AI/ML, or a related engineering field. [1, 2, 3]

📌 AI Test Engineer (or AI/ML Quality Assurance Engineer) (Hyderabad)
🏢 Teclusion
📍 Hyderabad