AI QUALITY • MODEL VALIDATION • RESPONSIBLE AI

Model Validation Lead IV

AI Model Validation & Responsible AI

Lead the validation of AI, Generative AI, and Agentic AI solutions while strengthening model governance, monitoring, risk management, and responsible AI practices across the enterprise.

Experience 6–8 Years
Location Bengaluru, India
Work Model Hybrid
Education Bachelor's Required
Apply Now ↗

Who Is This Role For?

This role is designed for an experienced AI model validation, model governance, machine learning, data science, or risk professional who can independently evaluate AI systems and translate technical findings into clear business and risk recommendations.

01

AI Validation Professional

Brings 6–8 years of experience in AI model validation, model governance, machine learning, data science, or a closely related discipline.

02

Responsible AI Practitioner

Understands fairness, robustness, explainability, reliability, responsible AI, and model risk management principles.

03

GenAI & Agentic AI Validator

Can evaluate LLMs, RAG applications, autonomous workflows, human-in- the-loop controls, escalation paths, and AI operational risks.

04

Data-Driven Decision Maker

Uses statistical analysis, dashboards, monitoring metrics, and validation evidence to make clear, defensible recommendations.

Strengthening AI Trust Through Independent Validation

The role is responsible for establishing and executing validation practices that help ensure AI solutions are safe, effective, reliable, explainable, and responsibly deployed across the enterprise.

Core Mission

Evaluate AI model performance, establish validation methodologies, define monitoring metrics, conduct independent validation reviews, and provide data-driven recommendations for informed AI deployment decisions.

What You Will Do

01

Define Validation Methodologies

Establish and execute practical validation approaches for AI, Generative AI, and emerging Agentic AI solutions.

02

Evaluate Model Performance

Assess accuracy, robustness, explainability, fairness, reliability, effectiveness, and other relevant dimensions of AI performance.

03

Validate Generative & Agentic AI

Evaluate Generative AI, Agentic AI, autonomous workflows, human-in-the-loop controls, escalation paths, and auditability.

04

Build Monitoring Frameworks

Develop model monitoring frameworks, performance dashboards, thresholds, and model health indicators to identify emerging risks.

05

Independent Validation Reviews

Conduct independent reviews of AI solutions and provide objective findings and recommendations to support deployment decisions.

06

AI Governance & Risk Management

Contribute to AI governance, model risk management, Responsible AI programs, monitoring activities, and lifecycle controls.

07

Cross-Functional Collaboration

Partner with Data Science, Product, Engineering, Risk, Compliance, and Business teams throughout the AI lifecycle.

08

Stakeholder Communication

Present validation results, risks, insights, and recommendations to technical and non-technical stakeholders.

From Model Assessment to Deployment Decision

A strong validation process evaluates the model from multiple dimensions before and after deployment, ensuring that performance and risk remain visible throughout the AI lifecycle.

1

Model Assessment

Evaluate model design, methodology, assumptions, and intended use.

2

Performance Testing

Measure accuracy, robustness, effectiveness, and statistical behavior.

3

Risk Evaluation

Assess fairness, explainability, operational risk, and responsible AI.

4

Monitoring

Track health indicators, thresholds, drift, and performance trends.

5

Deployment Decision

Provide evidence-based recommendations for informed deployment.

Experience & Technical Foundation

AI Model Validation

6–8 years of proven experience in AI model validation, model governance, machine learning, data science, or related roles.

Machine Learning

Strong understanding of ML algorithms, evaluation techniques, statistical methods, and AI performance measurement.

Model Monitoring

Experience building monitoring frameworks, dashboards, thresholds, and model health indicators.

Programming

Python or R proficiency, with familiarity with modern ML platforms and libraries.

What Should Be Validated?

Accuracy Measure whether model outputs meet expected performance levels.
Robustness Assess how reliably the model behaves under changing conditions.
Explainability Evaluate whether model behavior and outcomes can be understood.
Fairness Identify potential bias and inconsistent outcomes across groups.
Reliability Assess consistency and operational dependability of AI systems.
Effectiveness Determine whether AI solutions deliver their intended business outcomes.

Modern AI Validation Expertise

The role extends beyond traditional predictive models into Generative AI, Large Language Models, Retrieval-Augmented Generation, and Agentic AI systems.

Generative AI Large Language Models RAG Agentic AI Autonomous Workflows Human-in-the-Loop Escalation Paths AI Auditability Model Monitoring AI Performance Responsible AI Model Risk

Preferred Technical Exposure

Python R TensorFlow PyTorch Scikit-learn Databricks Azure AI Tableau Power BI

Responsible AI & Model Governance

The position contributes to responsible deployment by connecting technical validation with governance, model risk management, operational risk, and business decision-making.

01

Responsible AI

Support responsible development and deployment through fairness, explainability, robustness, and risk-focused validation.

02

Model Risk Management

Identify model risks, assess their potential impact, and provide evidence-based recommendations for mitigation.

03

Lifecycle Governance

Support monitoring, validation, change management, and controls throughout the AI lifecycle.

04

Regulated Industries

Experience in insurance, financial services, healthcare, or other regulated environments is an advantage.

Bengaluru-Based Hybrid Role

On-Site Requirement

The role requires the ability to work on-site in Bengaluru. The position follows a hybrid model, with Bengaluru as the primary base. Remote flexibility is available based on business needs and team alignment, while on-site presence remains essential for key engagements.

Key Behaviors

Driving Success

Independently drives work forward, anticipates barriers, takes ownership, and focuses on measurable business outcomes.

Improvement Mindset

Challenges existing approaches, identifies opportunities, develops solutions, and continuously improves AI validation practices.

Winning Together

Builds strong stakeholder relationships, communicates effectively, and encourages diverse perspectives across technical and business teams.

Build the Future of Responsible AI

Join an AI-focused organization where model validation, responsible AI, governance, and data-driven decision-making play a central role in expanding the safe and effective use of emerging technologies.

AI Innovation Responsible AI Leadership Professional Growth Learning & Development Stakeholder Impact Purpose-Driven Work

Lead the Validation of Responsible AI

Bring your expertise in AI model validation, Generative AI, Agentic AI, governance, monitoring, and risk management to help shape trustworthy enterprise AI.

Apply Now ↗