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.

Candidate Profile:

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

Mandatory Requirements:

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

- Hands-on experience building AI/ML solutions for Credit Risk, Fraud Risk Management (FRM), Collections & Recovery.

- Current designation must be Lead or above.

- Strong experience designing and deploying large-scale distributed Machine Learning systems.

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

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

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

- Experience working under BFSI governance, including PII handling, auditability, model governance, compliance, and secure-by-design architecture.

Education:

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

Age:

- Candidate's age should be below 37 years.

CTC:

- The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

Preferred Requirements:

- Candidates currently working as Lead, Principal, Engineering Manager, Associate Director, or Director in reputed Product, FinTech, Banking, NBFC, or Global Capability Centers.

- Indian professionals currently working overseas (NRI) who are planning to relocate and permanently settle in India.

- Experience building enterprise AI platforms using Graph ML, Vector Databases, LLM-enabled decisioning, distributed training frameworks, and large-scale AI infrastructure.