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
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:
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,machine learning,credit,management,risk,fraud,ml,learning,credit risk
