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We are seeking an experienced Risk Models Testing Lead to drive the testing, validation, and quality assurance of AI and Machine Learning models used across the banking ecosystem. The ideal candidate combines deep banking domain expertise with hands-on experience testing AI/ML models used for credit risk, fraud detection, AML, collections, customer segmentation, pricing, and regulatory reporting.

The role requires close collaboration with Data Scientists, Model Risk Management, Product Owners, Technology, Business, and Compliance teams to ensure AI solutions are accurate, explainable, compliant, and production-ready.

Location:

Flexible / Hybrid /Remote (Secondary)

Experience:

10–15 years of experience in Banking, Risk Management, AI/ML Product Delivery, or Model Validation.

AI/ML Model Testing & Validation

• Design and execute comprehensive testing strategies for AI and Machine Learning models.
• Validate model performance across training, validation, and production datasets.
• Perform functional, non-functional, regression, and end-to-end testing of AI-driven banking solutions.
• Validate model outputs against business expectations and regulatory requirements.
• Assess model accuracy, precision, recall, F1 score, ROC-AUC, and other relevant performance metrics.
• Test model robustness under edge cases, stress scenarios, and adverse conditions.

Banking Risk Models

• Lead testing and validation of models supporting:
• Credit Risk
• Probability of Default (PD)
• Loss Given Default (LGD)
• Exposure at Default (EAD)
• IFRS 9 Expected Credit Loss (ECL)
• Early Warning Systems
• Covenant Monitoring
• Fraud Detection
• AML Transaction Monitoring
• Collections Prioritization
• Customer Risk Rating
• Credit Decisioning
• Customer Segmentation
• Pricing and Limit Management

GenAI & LLM Testing

• Validate Large Language Model (LLM) outputs for banking use cases.
• Test prompt engineering strategies and dynamic prompting frameworks.
• Evaluate hallucinations, bias, consistency, explainability, and confidence scoring.
• Perform retrieval validation for Retrieval-Augmented Generation (RAG) applications.
• Test AI copilots supporting credit underwriting, policy interpretation, and customer servicing.

Model Governance

• Support Model Risk Management (MRM) lifecycle.
• Ensure compliance with model governance frameworks and validation standards.
• Validate model documentation, assumptions, limitations, and approval criteria.
• Ensure adherence to regulatory expectations from central banks and internal governance teams.

Data Validation

• Validate data quality, completeness, lineage, and feature engineering.
• Test data pipelines feeding AI models.
• Verify data transformations and feature calculations.
• Identify data drift, concept drift, and model performance degradation.

Automation & Quality Engineering

• Develop automated testing frameworks for AI models.
• Integrate AI testing into CI/CD pipelines.
• Maintain reusable test cases and regression suites.
• Produce detailed defect reports and testing dashboards.

Stakeholder Management

• Work with Risk, Business, Data Science, Compliance Audit, Technology, and Product teams.
• Present testing findings and model readiness assessments to governance forums.
• Support User Acceptance Testing (UAT) and production readiness reviews.

Banking Domain

• Corporate Banking
• Retail Banking
• Credit Risk
• Basel II / Basel III
• IFRS 9
• Regulatory Reporting
• Lending Lifecycle
• Credit Underwriting
• Risk Rating Models
• Fraud & AML

AI/ML

• Machine Learning lifecycle
• Model Validation
• Explainable AI (XAI)
• Responsible AI
• Prompt Engineering
• GenAI Testing
• LLM Evaluation
• RAG Validation
• Hallucination Detection
• Confidence Score Validation
• Bias & Fairness Testing

Technical Skills

• SQL
• Python
• Jupyter Notebook
• Git
• Azure DevOps / Jira
• REST APIs
• Snowflake
• Databricks (preferred)
• MLflow (preferred)

Testing Tools

• Selenium (optional)
• Postman
• PyTest
• Great Expectations
• Robot Framework
• JMeter
• API Testing
• Data Validation Tools

• 10+ years in Banking Technology, Risk, or Digital Transformation.
• Minimum 5 years of experience testing or validating AI/ML models.
• Experience working with Data Scientists and Model Risk teams.
• Strong understanding of model governance and regulatory expectations.
• Experience in Agile and DevSecOps environments.

• Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, or Finance.
• Master’s degree is an advantage.
• Certifications in AI/ML, Data Science, Risk Management, or Banking.
• ISTQB Certification (preferred).

• Strong analytical and critical thinking skills.
• Excellent stakeholder management and communication.
• Ability to translate complex AI concepts into business outcomes.
• High attention to detail and risk awareness.
• Strong problem-solving and decision-making capabilities.
• Leadership experience managing cross-functional teams and complex banking transformation initiatives