AI Model Validation Specialist | Machine Learning Validation Engineer

S.M.I.T (Saylani Mass I.T Training); Abu Dhabi; On-site; AED 223,000 - 502,000; Full time

Job summary

S.M.I.T (Saylani Mass I.T Training) in United Arab Emirates seeks an AI Model Validation Specialist to evaluate, validate, and monitor ML models for performance, robustness, fairness, and reliability. You will design validation plans, run benchmarks, and ensure responsible AI before and after deployment.

The role requires a strong foundation in statistics, ML engineering, and Python-based validation pipelines, with collaboration across data science, ML engineering, and risk teams.

Qualifications

• Bachelor’s or Master’s degree in a quantitative discipline.
• Proven experience in AI model validation or ML engineering.
• Strong understanding of supervised/unsupervised learning and evaluation principles.
• Proficiency in Python and ML libraries (scikit-learn, TensorFlow, PyTorch).
• Experience with statistical methods, experimental design, and data preprocessing.

Responsibilities

• Design and execute validation frameworks for ML models and AI apps.
• Assess model performance, reliability, robustness, and generalization.
• Review development methodologies, training procedures, and evaluation criteria.
• Develop validation plans, test cases, benchmarks, and acceptance criteria.

Skills

Analytical skills

Python

ML validation

Data science

Statistical methods

Experiment design

Cross-functional collaboration

Documentation

Communication

Problem solving

Education

Bachelor's or Master's in CS/DS/Statistics/Math/AI/ML

Tools

scikit-learn

pandas

NumPy

TensorFlow

PyTorch

SQL

Cloud ML services

Job description

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.

Key Responsibilities

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, underfitting, 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.

Requirements

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 model evaluation principles.

Proficiency in Python and experience with libraries such as scikit-learn, pandas, NumPy, TensorFlow, or PyTorch.

Strong understanding of statistical methods, probability, hypothesis testing, and experimental design.

Experience selecting and interpreting model performance metrics appropriate to different use cases.

Knowledge of data preprocessing, feature engineering, cross-validation, hyperparameter tuning, and model selection.

Experience identifying overfitting, underfitting, data leakage, selection bias, and other model-quality issues.

Understanding of model robustness, generalization, stability, and reliability testing.

Experience working with structured and unstructured datasets.

Familiarity with SQL and database systems for data extraction, analysis, and validation.

Experience with automated testing, test automation, or machine learning validation pipelines is advantageous.

Knowledge of model monitoring, data drift detection, concept drift, and production model evaluation.

Familiarity with MLOps tools, model registries, experiment tracking, and model lifecycle management is beneficial.

Understanding of AI governance, model risk management, explainability, fairness, and responsible AI principles.

Experience with explainability tools such as SHAP or LIME is advantageous.

Familiarity with cloud-based machine learning services and deployment environments is preferred.

Knowledge of large language model evaluation, prompt testing, retrieval-augmented generation (RAG) evaluation, or generative AI validation is beneficial.

Strong analytical, critical-thinking, and problem-solving skills.

Excellent attention to detail and a systematic approach to testing and validation.

Ability to interpret complex model results and communicate findings clearly.

Strong technical documentation and report-writing skills.

Ability to collaborate effectively with technical teams, business stakeholders, and risk professionals.

Ability to manage multiple validation projects and meet deadlines.

Commitment to reproducibility, data integrity, independent assessment, and professional standards.

Relevant certifications in machine learning, data science, cloud AI, or model risk management are advantageous.

What We Offer

Competitive salary and comprehensive employee benefits.

Opportunity to work on advanced AI and machine learning technologies.

Exposure to model validation, AI governance, statistical testing, and responsible AI practices.

Professional development in machine learning engineering, model evaluation, and AI risk management.

Training and support for relevant technical certifications.

Opportunities to work with data scientists, AI engineers, software developers, and risk specialists.

Access to modern development tools, analytical platforms, and machine learning technologies.

Opportunities to contribute to AI innovation, automation, and enterprise technology projects.

Recognition for improving model reliability, performance, and validation standards.

• Career progression toward Senior AI Model Validation Specialist, Machine Learning Validation Lead, AI Risk Manager, Model Validation Manager, or Head of AI Governance and Validation.