Company: Intuit

Job Title: Lead Machine Learning Engineer / AI Product Pod Lead Credit Risk, Fraud & Collections

Location: Bengaluru, India

Employment Type: Full-Time

About Intuit

Intuit is the global financial technology platform powering prosperity for over 100 million customers worldwide. Driven by AI-led innovation, flagship products like TurboTax, QuickBooks, Credit Karma, and Mailchimp empower consumers, small businesses, and self-employed individuals to save money, secure capital, and streamline operations with confidence.

Role Overview

As a Lead Machine Learning Engineer / AI Product Pod Lead, you will lead technical pods dedicated to building production-scale AI systems across the end-to-end credit lifecycleincluding Credit Risk, Fraud Risk Management (FRM), and Collections Analytics. You will architect distributed ML systems, build feature stores, design MLOps pipelines, and deploy AI solutions driving real-time financial decisioning under strict BFSI governance.

Key Skills

- Core Engineering & Systems: Python, Apache Spark, Apache Kafka, Kubernetes, Distributed Computing, APIs/Microservices, CI/CD.
- ML Infrastructure & MLOps: Feature Store, Model Registry, Model Governance, Model Monitoring & Explainability.
- Domain & AI Methodologies: Machine Learning, Credit Risk Analytics, Fraud Analytics, Collections Analytics, Graph ML, Vector Databases, LLM-enabled Decisioning.

Roles & Responsibilities

- Technical Leadership: Lead cross-functional AI Product Pods across Credit Risk, Fraud, and Collections functions.
- End-to-End Production ML: Build, train, fine-tune, evaluate, deploy, and scale production-grade ML systems for lending lifecycle decisioning.
- Distributed AI Infrastructure: Design scalable distributed ML platforms, feature stores, model registries,



and automated CI/CD MLOps pipelines.
- Domain AI Solutions: Develop AI solutions for underwriting, portfolio risk monitoring, fraud detection, anomaly detection, Graph ML-based risk modeling, and recovery optimization.
- Governance & Regulatory Standards: Ensure robust model governance, auditability, explainability, PII data protection, and adherence to BFSI regulatory compliance.
- Operational Excellence: Define platform architecture, establish SLAs, oversee incident management, and maintain model risk management practices.
- Cross-Functional Collaboration: Partner closely with Product, Risk, Data, Engineering, and Business leaders to drive quantifiable business outcomes.
- People Leadership: Build, mentor, and scale high-performing AI Engineering and Data Science teams.

Candidate Requirements

Mandatory Qualifications

1. Current Role: Currently serving as a Lead Data Scientist, Lead AI Engineer, Lead ML Engineer, or higher designation.
2. Total Experience: 10+ years of hands-on experience in Data Science, AI/ML, or AI Engineering, with a proven track record of shipping production-grade ML systems.
3. Domain Expertise: Proven track record building AI/ML solutions specifically in Credit Risk, Fraud Risk Management (FRM), or Collections & Recovery within FinTech/BFSI.
4. Large-Scale Systems: Deep experience designing distributed ML architectures, including model training,



inference, and scalable serving infrastructure.
5. Tech Stack Proficiency: Solid programming experience in Python, with hands-on exposure to Spark, Kafka, Kubernetes, APIs, CI/CD, Feature Stores, and Model Registries.
6. Modeling Expertise: Hands-on experience designing Credit Risk Models, Fraud Detection, Graph ML, Early Warning Systems (EWS), Portfolio Monitoring, Collections Optimization, Propensity Models, and Recovery Forecasting.
7. Team & Delivery Leadership: Proven experience leading technical teams, managing production platforms, and driving cross-functional project execution.
8. BFSI Compliance: Experience operating under BFSI data governance standards (PII handling, approval workflows, auditability, secure-by-design architecture).
9. Education: Bachelors or Master’s degree (B.Tech / M.Tech) from Tier-1 institutions (IITs, NITs, BITS).
10. Age Limit: Below 37 years.

Compensation Structure

- CTC Split: 75% Fixed + 25% Variable (Performance Bonus/Incentives), strictly per company policy.

Preferred Qualifications

- Candidates currently working as Lead, Principal, Engineering Manager, Associate Director, or Director in reputed Product FinTechs, Banks, NBFCs, or Global Capability Centers (GCCs).
- Non-Resident Indians (NRIs) planning to relocate and permanently settle in India are strongly encouraged to apply.
- Experience building enterprise AI platforms leveraging Graph ML, Vector Databases, LLM-enabled decisioning, or distributed training frameworks.

How to Apply : Fill The Application Form ( Usually takes less than 30 sec) -

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