Roles & Responsibilities :

- Lead AI Product Pods across Credit Risk, Fraud, and Collections functions.

- Build and deploy production-scale Machine Learning systems for lending lifecycle decisioning.

- Own complete ML lifecycle including feature engineering, model training, evaluation, deployment, monitoring, and continuous improvement.

- Design scalable distributed ML infrastructure, feature stores, model registries, and MLOps pipelines.

- Develop AI solutions for underwriting, portfolio risk monitoring, fraud detection, anomaly detection, and recovery optimization.

- Drive model governance, monitoring, explainability, and compliance within BFSI regulatory standards.

- Collaborate with Product, Risk, Engineering, Data, and Business teams to deliver AI-driven business outcomes.

- Define AI platform architecture, operational excellence, SLAs, and incident management practices.

- Build, mentor, and scale high-performing AI Engineering and Data Science teams.

Ideal Candidate :

- 1. Strong Lead Data Science, AI Engineer, or Machine Learning Engineer profiles.

- 2. Must have 10+ years of experience in Data Science, AI/ML or AI Engineering with hands-on experience building production-grade ML systems.

- 3. Must have hands-on experience building AI/ML solutions for Credit Risk, Fraud Risk Management (FRM), Collections & Recovery, with proven delivery of business-impacting AI/ML solutions.

- 4. Candidate's current designation must be Lead or above.

- 5. Must have strong experience designing and deploying large-scale distributed Machine Learning systems, including model training, fine-tuning, inference, scalable serving, and production deployment.

- 6. Strong programming experience in Python, along with exposure to Spark, Kafka, Kubernetes, APIs/Microservices, CI/CD, Feature Store, Model Registry, and Distributed Computing.

- 7. Experience designing and deploying Credit Risk Models, Fraud Detection Models, Graph ML, Early Warning Systems, Portfolio Monitoring, Collections Optimization, Propensity Models, and Recovery Forecasting.

- 8. Proven experience leading AI/ML teams, owning end-to-end delivery, mentoring engineers, driving cross-functional execution, and managing production AI platforms.

- 9. Must have experience working under BFSI governance, including PII handling, auditability, model governance, compliance, secure-by-design architecture, approval workflows, and model risk management practices.

- 10. B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are considered.

- 11. Candidate's age should be below 3 - 7 years.