Risk Models Testing Lead – AI/ML (Banking)

Marktine Technology Solutions Pvt. Ltd.; United States; Remote; USD 150,000 - 230,000; Full time

Job summary

Marktine Technology Solutions Pvt. Ltd. seeks an experienced AI/ML Testing & Validation expert for banking domains. You will design comprehensive test strategies, validate model outputs, and ensure regulatory alignment across PD, LGD, EAD, IFRS 9, and ECL risk areas.

You will collaborate with Risk, Data Science, Compliance, and IT teams to support model risk management, governance, and UAT, while advancing GenAI/LLM testing and RAG validation in production-ready pipelines.

Qualifications

• 10+ years in Banking Technology, Risk, or Digital Transformation.
• Minimum 5 years of experience testing or validating AI/ML models.
• Experience with Data Scientists and Model Risk teams; knowledge of governance.

Responsibilities

• Design and execute testing strategies for AI/ML models in banking applications.
• Validate model performance across training/validation/production datasets.
• Assess metrics like accuracy, precision, recall, F1, ROC-AUC.
• Perform end-to-end testing of AI-driven banking solutions and governance.

Skills

Banking domain

Credit risk

IFRS 9

Regulatory reporting

Lending lifecycle

Credit underwriting

Risk rating models

Fraud & AML

AI/ML

Machine learning lifecycle

Model validation

Explainable AI

Prompt engineering

GenAI testing

LLM evaluation

RAG validation

Hallucination detection

Confidence score validation

Bias & fairness testing

SQL

Python

Jupyter Notebook

Git

Azure DevOps / Jira

REST APIs

Snowflake

Databricks

MLflow

Tools

Selenium

Postman

PyTest

Great Expectations

Robot Framework

JMeter

API Testing

Data Validation Tools

Job description

Key Responsibilities

AI/ML Model Testing & ValidationDesign 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 ModelsLead testing and validation of models supporting:Credit RiskProbability of Default (PD)Loss Given Default (LGD)Exposure at Default (EAD)IFRS 9 Expected Credit Loss (ECL)Early Warning SystemsCovenant MonitoringFraud DetectionAML Transaction MonitoringCollections PrioritizationCustomer Risk RatingCredit DecisioningCustomer SegmentationPricing and Limit ManagementGenAI & LLM TestingValidate 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 GovernanceSupport 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 ValidationValidate 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 EngineeringDevelop 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 ManagementWork 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.Required SkillsBanking DomainCorporate BankingRetail BankingCredit RiskBasel II / Basel IIIIFRS 9Regulatory ReportingLending LifecycleCredit UnderwritingRisk Rating ModelsFraud & AMLAI/MLMachine Learning lifecycleModel ValidationExplainable AI (XAI)Responsible AIPrompt EngineeringGenAI TestingLLM EvaluationRAG ValidationHallucination DetectionConfidence Score ValidationBias & Fairness TestingTechnical SkillsSQLPythonJupyter NotebookGitAzure DevOps / JiraREST APIsSnowflakeDatabricks (preferred)MLflow (preferred)Testing ToolsSelenium (optional)PostmanPyTestGreat ExpectationsRobot FrameworkJMeterAPI TestingData Validation ToolsExperience Required10+ 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.Preferred QualificationsBachelor’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).

Key Competencies

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