Model Validation Engineer

Model Validation EngineerWe are seeking a Model Validation Engineer to evaluate, validate, and monitor machine learning and AI models to ensure they are accurate, reliable, robust, fair, and compliant with organizational and regulatory standards. The ideal candidate will work closely with data scientists, ML engineers, and risk teams to assess model performance, identify weaknesses, and recommend improvements before and after deployment.Key ResponsibilitiesIndependently validate machine learning, deep learning, and generative AI models before production deployment.Assess model performance using appropriate statistical and machine learning evaluation metrics.Design and execute validation plans, test cases, and benchmarking methodologies.Evaluate models for robustness, stability, fairness, explainability, and reliability.Perform stress testing, sensitivity analysis, and scenario testing.Validate data quality, feature engineering, and training pipelines.Assess risks related to overfitting, data drift, concept drift, and model degradation.Review model assumptions, documentation, and development methodologies.Develop automated validation frameworks and monitoring dashboards.Collaborate with ML engineers, data scientists, software engineers, and governance teams.Produce validation reports and recommend remediation actions.Support model governance, audit readiness, and regulatory compliance.Required QualificationsBachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Finance, or a related field.3–5+ years of experience in machine learning, data science, model validation, or analytics.Strong understanding of machine learning algorithms and statistical modeling.Experience validating predictive or AI models in production environments.Proficiency in Python and SQL.Experience working with structured and unstructured datasets.Preferred QualificationsMaster's degree in Data Science, Statistics, Computer Science, Applied Mathematics, or a related field.Experience validating Large Language Models (LLMs) and Generative AI applications.Knowledge of Responsible AI principles and AI governance.Professional certifications in AI, cloud platforms, or data science.Experience in regulated industries such as banking, healthcare, or insurance.Technical SkillsPython (Pandas, NumPy, Scikit-learn)SQLStatistical analysis and hypothesis testingMachine learning algorithmsDeep learning fundamentalsModel evaluation metrics (Accuracy, Precision, Recall, F1 Score, ROC-AUC, RMSE, MAE)Cross-validation techniquesFeature engineering validationExplainable AI (SHAP, LIME)Fairness and bias assessmentModel monitoring and drift detectionData quality validationGit and CI/CD pipelinesCloud platforms (AWS, Azure, Google Cloud)MLflow or similar model lifecycle toolsData visualization (Power BI, Tableau, Matplotlib)Soft SkillsAnalytical thinkingCritical reasoningProblem-solvingStrong documentation skillsCommunication and presentationAttention to detailCollaboration with cross-functional teamsTime managementContinuous learningPreferred ExperienceAI/ML model validationCredit risk, fraud detection, or forecasting modelsLarge Language Models (LLMs)Generative AI applicationsMLOps environmentsEnterprise AI platformsRegulated industries (Finance, Healthcare, Insurance)Success MetricsModel validation accuracy and completenessReduction in production model failuresTimely completion of validation reviewsDetection of model risks before deploymentModel performance monitoring effectivenessCompliance with governance and regulatory requirementsQuality of validation documentationStakeholder satisfactionNice-to-Have SkillsResponsible AI frameworksAI safety evaluationLLM evaluation frameworks (e.g., DeepEval, Ragas, LangSmith)Prompt engineeringRetrieval-Augmented Generation (RAG) validationDocker and KubernetesApache SparkMLOps platforms (Kubeflow, SageMaker, Vertex AI)Risk management frameworksFamiliarity with model governance standards (e.g., SR 11-7 or equivalent)