Role Overview :

A QA Engineer for AI Initiatives is responsible for ensuring the quality, reliability, fairness, and performance of AI/ML-powered products and systems. Unlike traditional QA, this role requires deep understanding of non-deterministic model behavior, data quality, and AI-specific failure modes such as hallucinations, bias, and model drift.

Key Responsibilities :

- Design and execute test strategies specifically for AI/ML models, LLM-based applications, and data pipelines

- Develop automated test frameworks for model validation, regression testing, and performance benchmarking

- Evaluate model outputs for accuracy, consistency, relevance, hallucination, and bias across diverse inputs

- Test RAG (Retrieval-Augmented Generation) pipelines, chatbots, recommendation systems, and other AI-driven features

- Collaborate with data scientists and ML engineers to define acceptance criteria and quality thresholds

- Build and maintain evaluation datasets, ground truth sets, and adversarial test cases

- Monitor models in production for drift, degradation, and anomalous behavior

- Validate data quality, data pipelines, and feature stores that feed AI systems

- Document defects, edge cases, and failure patterns specific to AI behavior

- Ensure AI systems meet ethical, fairness, and compliance standards (bias audits, explainability checks)

Required Skills & Qualifications :

- Bachelor's or Master's degree in Computer Science, Engineering, or a related field

- 3-6 years of QA experience, with at least 1-2 years in AI/ML quality assurance

- Strong proficiency in Python for test automation and data analysis

- Familiarity with LLM evaluation frameworks (e.g., RAGAS, DeepEval, Promptfoo, LangSmith)

- Hands-on experience with testing tools : Pytest, Selenium, Postman, or similar

- Understanding of ML lifecycle - training, validation, deployment, and monitoring

- Knowledge of data quality tools and pipeline testing (Great Expectations, dbt tests)