Lead Data Scientist / AI/ML Engineer

Roles &

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 / Machine Learning Engineer profiles. 2 Mandatory (Experience 1) - Must have 10+ years of experience in Data Science, AI/ML or AI Engineering with hands-on experience building production-grade ML systems. 3 Mandatory (Experience 2) - 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 Mandatory (Experience 3) - Candidate's Current designation must be Lead or above. 5 Mandatory (Experience 4) - 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 Mandatory (Experience 5) - Strong programming experience in Python, along with exposure to Spark, Kafka, Kubernetes, APIs/Microservices, CI/CD, Feature Store, Model Registry, and Distributed Computing. 7 Mandatory (Experience 6) - 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 Mandatory (Experience 7) - Proven experience leading AI/ML teams, owning end-to-end delivery, mentoring engineers, driving cross-functional execution, and managing production AI platforms. 9 Mandatory (Experience 8) – 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 Mandatory (