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Risk Models Testing Lead – AI/ML (Banking)

c2c,

at Marktine Technology Solutions Pvt. Ltd.

Posted by Princy Jain

Experience8 - 12 Years
SalaryNot Disclosed
Job TypeFull Time
Location Remote
Job Highlights

Role-Risk Models Testing Lead – AI/ML (Banking)
Location Flexible / Remote 
Experience 8–12 years

Role Summary

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.

Job Description

Key Responsibilities 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. Required Skills 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 Experience Required 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. Preferred Qualifications 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). 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

Education

UG :Any UG Qualification

PG :Any PG Qualification

Key Skills
10+ years in banking technology risk or digital transformation
5+ years testing or validating ai/ml models
banking
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 model testing
model validation
functional testing
non-functional testing
regression testing
end-to-end testing
model performance assessment
model accuracy evaluation
precision evaluation
recall evaluation
f1 score evaluation
roc-auc evaluation
model robustness testing
stress testing
edge case testing
banking risk model testing
credit risk model testing
probability of default testing
loss given default testing
exposure at default testing
ifrs 9 expected credit loss testing
early warning systems testing
covenant monitoring testing
fraud detection testing
aml transaction monitoring
collections prioritization testing
customer risk rating testing
credit decisioning testing
customer segmentation testing
pricing and limit management testing
genai testing
large language model output validation
prompt engineering testing
dynamic prompting framework testing
hallucination detection
bias testing
consistency testing
explainability testing
confidence score validation
retrieval-augmented generation validation
ai copilot testing
model governance support
model risk management lifecycle support
model documentation validation
assumptions validation
limitations validation
approval criteria validation
regulatory compliance validation
data quality validation
data completeness validation
data lineage validation
feature engineering validation
data pipeline testing
data transformation verification
feature calculation verification
data drift identification
concept drift identification
model performance degradation identification
automated testing framework development
ai testing integration into ci/cd
reusable test case maintenance
regression suite maintenance
defect reporting
testing dashboard production
user acceptance testing support
production readiness review support
sql
python
jupyter notebook
git
azure devops
jira
rest apis
snowflake
postman
pytest
great expectations
robot framework
jmeter
analytical thinking
critical thinking
stakeholder management
communication
risk awareness
problem-solving
decision-making
bachelor's degree in computer science
bachelor's degree in engineering
bachelor's degree in mathematics
bachelor's degree in statistics
bachelor's degree in finance
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