AI Model Validation Specialist | Machine Learning Validation Engineer

Role overview

Validation and testing

• Design validation frameworks, plans, test cases, benchmarks, and acceptance criteria.
• Assess model methods, assumptions, algorithms, training, evaluation, and performance metrics.
• Run statistical, sensitivity, stress, scenario-based, and unseen-data tests.
• Validate data, preprocessing, and feature engineering; identify quality issues, bias, imbalance, and leakage.
• Assess stability and generalization across datasets, segments, conditions, and time periods.
• Evaluate explainability, fairness, responsible AI, and generative AI systems where applicable.

Production monitoring and governance

• Build automated validation scripts, pipelines, monitoring metrics, dashboards, and alerts.
• Review production readiness and monitor for drift, degradation, and prediction-quality changes.
• Investigate incidents, support root-cause analysis, retraining, recalibration, and revalidation.
• Document results and recommendations; contribute to model risk assessment, governance, and lifecycle management.
• Collaborate with data, engineering, software, and risk teams; research validation methods and recommend process improvements.

Qualifications

• Bachelor’s or Master’s degree in a quantitative discipline such as computer science, data science, statistics, mathematics, AI, or machine learning.
• Proven experience in AI validation, machine learning engineering, data science, model testing, or statistical analysis.
• Knowledge of learning methods, statistics, model metrics, preprocessing, feature engineering, validation, and common model-quality issues.
• Proficiency in Python and experience with libraries such as scikit-learn, pandas, NumPy, TensorFlow, or PyTorch.
• Experience with structured and unstructured data; familiarity with SQL and databases.
• Knowledge of model monitoring, drift, explainability, fairness, AI governance, and responsible AI principles.
• Automated testing, MLOps, SHAP/LIME, cloud ML, generative AI evaluation, and relevant certifications are advantageous or preferred.
• Strong analytical and problem-solving skills, attention to detail, clear communication, documentation, collaboration, and project management.

What the employer offers

• Competitive salary and comprehensive employee benefits.
• Work with advanced AI technologies, modern tools, and technical and risk teams.
• Professional development, training, certification support, and potential career progression in validation and AI governance.