AI/ML Engineering Manager

About The Role We are looking for an experienced Lead AI/ML Engineering Manager to lead AI Product Pods across Credit Risk, Fraud Risk Management (FRM), and Collections & Recovery. The ideal candidate will have extensive experience building and deploying production-grade Machine Learning systems that deliver measurable business impact across the lending lifecycle. This role requires strong technical leadership, hands-on AI/ML expertise, and experience leading high-performing engineering teams within regulated BFSI environments. Key Responsibilities Lead AI Product Pods across Credit Risk, Fraud Risk Management, and Collections functions. Design, develop, and deploy production-scale Machine Learning systems for lending lifecycle decisioning. Own the complete ML lifecycle including: Feature Engineering Model Training Model Evaluation Deployment Monitoring Continuous Improvement Design scalable ML platforms including: Distributed ML Infrastructure Feature Stores Model Registry MLOps Pipelines Develop AI solutions for: Credit Underwriting Credit Risk Models Fraud Detection Graph ML Early Warning Systems Portfolio Risk Monitoring Collections Optimisation Recovery Forecasting Propensity Models Drive model governance, explainability, monitoring, compliance, and audit readiness in accordance with BFSI regulations. Collaborate with Product, Engineering, Risk, Data Science, and Business stakeholders to deliver AI-driven outcomes. Define AI platform architecture, operational excellence, SLAs, production monitoring, and incident management practices. Build, mentor, and scale high-performing AI Engineering and Data Science teams. Mandatory Requirements Experience 10+ years of experience in AI Engineering, Data Science, or Machine Learning. Current designation must be Lead or above (Lead / Principal / Engineering Manager / Associate Director / Director). Strong experience building production-grade Machine Learning systems. Hands-on experience developing AI/ML solutions in: Credit Risk Fraud Risk Management (FRM) Collections & Recovery Proven experience designing and deploying large-scale distributed ML systems, including: Model Training Fine-tuning Inference Scalable Model Serving Production Deployment Strong leadership experience managing AI/ML teams and delivering end-to-end enterprise AI platforms. Technical Skills Programming Python (Expert) AI/ML Machine Learning Deep Learning Feature Engineering Model Explainability Model Monitoring MLOps Distributed Machine Learning Big Data & Infrastructure Apache Spark Apache Kafka Kubernetes Distributed Computing REST APIs / Microservices CI/CD Pipelines Feature Store Model Registry AI Solutions Experience Must have experience building one or more of the following: Credit Risk Models Fraud Detection Models Graph Machine Learning Early Warning Systems Portfolio Monitoring Collections Optimisation Recovery Forecasting Propensity Models BFSI Governance Hands-on Experience With PII Handling Model Governance Model Risk Management Auditability Compliance Secure-by-Design Architecture Approval Workflows Educational Qualification Mandatory B.Tech / M.Tech from Tier-1 institutes: IITs NITs BITS Pilani Preferred Qualifications Candidates currently working as: Lead AI Engineer Principal AI Engineer Engineering Manager Associate Director Director From Reputed Product Companies FinTech Organisations Banks NBFCs Global Capability Centres (GCCs) will be preferred. Indian professionals currently working overseas (NRI) and planning permanent relocation to India are encouraged to apply. Experience With The Following Is Highly Preferred Graph ML Vector Databases LLM-enabled Decisioning Systems Distributed Training Frameworks Enterprise AI Platforms Large-scale AI Infrastructure Skills: data,learning,credit,ml,management,credit risk,machine learning,fraud,risk Show more