AI Model Validation Specialist | Machine Learning Validation Engineer

Employment Type: Full-Time

Level: Mid-Level to Senior

We are seeking a highly analytical and technically skilled AI Model Validation Specialist | Machine Learning Validation Engineer to evaluate, validate, and monitor the performance, reliability, robustness, and fairness of artificial intelligence and machine learning models. This role combines model validation, statistical analysis, machine learning engineering, risk assessment, and performance testing to ensure AI systems meet business requirements, technical standards, and responsible AI principles before and after deployment.

• Design and execute validation frameworks for machine learning models and AI-driven applications.
• Independently assess model performance, accuracy, reliability, robustness, and generalization capabilities.
• Review model development methodologies, assumptions, algorithms, training procedures, and evaluation criteria.
• Develop validation plans, test cases, benchmarks, and acceptance criteria for AI models.
• Evaluate classification, regression, forecasting, recommendation, and other machine learning models.
• Assess model performance using appropriate metrics, including precision, recall, F1-score, ROC-AUC, RMSE, MAE, and other relevant measures.
• Perform statistical testing, sensitivity analysis, stress testing, and scenario-based validation.
• Evaluate model behavior on unseen data and identify overfitting, under fitting, data leakage, and generalization issues.
• Validate training datasets, testing datasets, feature engineering processes, and data preprocessing pipelines.
• Identify data-quality problems, sampling bias, class imbalance, missing values, and inconsistencies that may affect model outcomes.
• Assess model stability and performance across different datasets, customer segments, operating conditions, and time periods.
• Conduct bias, fairness, explainability, and responsible AI assessments where applicable.
• Evaluate model interpretability using techniques such as SHAP, LIME, feature importance, and other explainability methods.
• Test model resilience against noisy inputs, edge cases, unexpected data distributions, and potential adversarial scenarios.
• Validate large language models and generative AI systems for factual consistency, relevance, safety, reliability, and task-specific performance where applicable.
• Develop automated testing scripts and validation pipelines using Python and relevant machine learning libraries.
• Perform independent benchmarking against baseline models, alternative algorithms, and predefined business requirements.
• Review model outputs to identify anomalies, unstable predictions, and unexpected behavior.
• Support model risk assessments and evaluate the potential business impact of model limitations or failures.
• Establish model validation documentation, testing evidence, performance reports, and remediation recommendations.
• Collaborate with data scientists, machine learning engineers, software developers, data engineers, and risk-management teams.
• Review model deployment processes and assess readiness for production implementation.
• Monitor deployed models for data drift, concept drift, performance degradation, and changes in prediction quality.
• Develop monitoring metrics, validation dashboards, and automated alerts for model performance issues.
• Investigate model incidents and coordinate root-cause analysis and corrective actions.
• Support model retraining, recalibration, optimization, and revalidation activities.
• Evaluate model version changes and verify that updates do not introduce unexpected performance regressions.
• Ensure validation activities are reproducible, traceable, and aligned with internal policies and relevant industry standards.
• Contribute to model governance, model lifecycle management, and responsible AI initiatives.
• Recommend improvements to model development, testing methodologies, validation standards, and deployment controls.
• Research emerging validation techniques, machine learning technologies, AI evaluation frameworks, and industry best practices.

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, Machine Learning, or a related quantitative discipline.
• Proven experience in AI model validation, machine learning engineering, data science, model testing, or statistical analysis.
• Strong understanding of supervised learning, unsupervised learning, deep learning, and…