Model Validation Engineer

Job Title
Model Validation Engineer
Location
Hybrid / Remote
Employment Type
Full-time
Job Summary
We 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 Responsibilities

• Independently 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 Qualifications

• Bachelor'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 Qualifications

• Master'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 Skills

• Python (Pandas, NumPy, Scikit-learn)
• SQL
• Statistical analysis and hypothesis testing
• Machine learning algorithms
• Deep learning fundamentals
• Model evaluation metrics (Accuracy, Precision, Recall, F1 Score, ROC-AUC, RMSE, MAE)
• Cross-validation techniques
• Feature engineering validation
• Explainable AI (SHAP, LIME)
• Fairness and bias assessment
• Model monitoring and drift detection
• Data quality validation
• Git and CI/CD pipelines
• Cloud platforms (AWS, Azure, Google Cloud)
• MLflow or similar model lifecycle tools
• Data visualization (Power BI, Tableau, Matplotlib)

Soft Skills

• Analytical thinking
• Critical reasoning
• Problem-solving
• Strong documentation skills
• Communication and presentation
• Attention to detail
• Collaboration with cross-functional teams
• Time management
• Continuous learning

Preferred Experience

• AI/ML model validation
• Credit risk, fraud detection, or forecasting models
• Large Language Models (LLMs)
• Generative AI applications
• MLOps environments
• Enterprise AI platforms
• Regulated industries (Finance, Healthcare, Insurance)

Success Metrics

• Model validation accuracy and completeness
• Reduction in production model failures
• Timely completion of validation reviews
• Detection of model risks before deployment
• Model performance monitoring effectiveness
• Compliance with governance and regulatory requirements
• Quality of validation documentation
• Stakeholder satisfaction

Nice-to-Have Skills

• Responsible AI frameworks
• AI safety evaluation
• LLM evaluation frameworks (e.g., DeepEval, Ragas, LangSmith)
• Prompt engineering
• Retrieval-Augmented Generation (RAG) validation
• Docker and Kubernetes
• Apache Spark
• MLOps platforms (Kubeflow, SageMaker, Vertex AI)
• Risk management frameworks
• Familiarity with model governance standards (e.g., SR 11-7 or equivalent)