AI/ML Testing Engineer (AI Quality Engineer)

About the role

The AI Quality Engineer will validate cutting‑edge artificial‑intelligence solutions across multiple domains, including generative, conversational, and predictive AI. You will ensure model performance, reliability, and compliance before production deployment.

Key responsibilities

• Design and execute test plans for AI/ML models, covering data preparation, feature engineering, training, validation, and tuning.
• Validate model outputs using Python‑based automation, pandas, NumPy, and scikit‑learn.
• Perform data quality analysis, profiling, and feature validation.
• Test AI APIs and services built with FastAPI or Flask, including performance, latency, and scalability assessments.
• Validate cloud‑based AI deployments, preferably on AWS SageMaker, and monitor production ML lifecycle.
• Collaborate with cross‑functional teams to interpret model behavior, explainability, and evaluation metrics.

Required profile

• Strong analytical and problem‑solving abilities.
• Excellent communication skills for cross‑functional collaboration.
• Experience with AI/ML testing in regulated or high‑risk domains is a plus.

Required skills

• Python programming for test automation and data analysis.
• pandas, NumPy, scikit‑learn.
• Understanding of core ML algorithms (regression, classification, clustering, tree‑based models, neural networks, transformers).
• Experience testing Generative AI, Conversational AI, and Predictive models.
• Testing AI APIs built with FastAPI or Flask.
• Familiarity with AWS SageMaker or similar cloud AI services.
• Knowledge of model evaluation metrics and validation techniques.