AI/ML Risk Model Testing Lead (Banking)

Code1 Tech Systems; Hinoba-an; Hybrid; PHP 1,800,000 - 3,200,000; Full time

Job summary

Code1 Tech is seeking an experienced Risk Models Testing Lead to drive testing, validation, and QA of AI/ML 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

Qualifications

• Banking domain experience in risk, Basel II/III, IFRS 9, regulatory reporting.
• Experience testing/validating AI/ML models in banking contexts.
• Strong understanding of model governance and regulatory expectations.

Responsibilities

• Design and execute comprehensive testing strategies for AI/ML 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.
• Assess model outputs against business expectations and regulatory requirements.
• Lead testing and validation for PD, EAD, ECL, and related risk modules.
• Coordinate with Data Science, Model Risk, Business, Technology, and Compliance teams.

Skills

Banking domain knowledge

AI/ML lifecycle

Explainable AI (XAI)

Prompt engineering

GenAI testing

LLM evaluation

RAG validation

Bias & fairness testing

Stakeholder management

Leadership

Education

Bachelor’s degree in CS/Engineering/Math/Finance

Master’s degree (advantage)

Certifications in AI/ML or Risk Management

ISTQB (preferred)

Tools

SQL

Python

Jupyter Notebook

Git

REST APIs

Snowflake

Databricks

MLflow

Job description

We Are

At Code1 Tech, we drive innovations that shape the future of enterprise technology. Our expertise spans Data Engineering, AI/ML, Cloud Solutions, and Full-Stack Development. We empower businesses with cutting-edge technology solutions, enabling digital transformation at scale. Join us to build impactful products with a passionate team of engineers and innovators.

About The Role

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.

Key Responsibilities:

• 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.
• Lead testing and validation of models supporting:
• Credit Risk
• Probability of Default (PD)
• Exposure at Default (EAD)
• IFRS 9 Expected Credit Loss (ECL)
• Early Warning Systems
• AML Transaction Monitoring
• Collections Prioritization
• Credit Decisioning
• 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.
• 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.
• Verify data transformations and feature calculations.

Automation & Quality Engineering

• Develop automated testing frameworks for AI models.
• 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.

Must Have Skills:

Banking Domain

• Retail Banking
• Credit Risk
• Basel II / Basel III
• IFRS 9
• Regulatory Reporting
• Lending Lifecycle

AI/ML

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

Technical Skills

• SQL
• Python
• Jupyter Notebook
• Git
• REST APIs
• Snowflake
• Databricks (preferred)
• MLflow (preferred)

Testing Tools

• Postman
• PyTest
• 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).
• 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