Total Experience range required -10 to 12 years
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
- 10+ years of experience in Data Science, AI/ML, or AI Engineering, with hands-on expertise in building and deploying production-grade machine learning systems.
- Proven experience developing AI/ML solutions for Credit Risk, Fraud Risk Management (FRM), Collections & Recovery, delivering measurable business impact.
- Currently working in a Lead-level or higher role (Lead, Principal, Engineering Manager, Associate Director, Director, etc.).
- Strong expertise in designing and deploying large-scale distributed machine learning systems, including model training, fine-tuning, inference, scalable model serving, and production deployment.
- Excellent programming skills in Python, with hands-on experience in Spark, Kafka, Kubernetes, APIs/Microservices, CI/CD pipelines, Feature Stores, Model Registry, and Distributed Computing.
- Experience building and deploying Credit Risk Models, Fraud Detection Models, Graph ML solutions, Early Warning Systems, Portfolio Monitoring, Collections Optimization, Propensity Models, and Recovery Forecasting.
- Demonstrated experience leading AI/ML teams, owning end-to-end solution delivery, mentoring engineers, collaborating with cross-functional stakeholders, and managing production AI platforms.
- Strong understanding of BFSI governance, including PII handling, model governance, auditability, compliance, secure-by-design architecture, approval workflows, and model risk management.
- B.Tech/M.Tech from Tier 1 institutes such as IITs, NITs, or BITS.
- Age below 37 years.
- Compensation structure will be 75% fixed and 25% variable, as per company policy.
Preferred Qualifications
- Currently working with a Product Company, FinTech, Banking, NBFC, or Global Capability Center (GCC) in roles such as Lead, Principal, Engineering Manager, Associate Director, or Director.
- Indian professionals currently working overseas (NRIs) who are planning to relocate and settle in India are encouraged to apply.
- Experience building enterprise AI platforms using Graph ML, Vector Databases, LLM-enabled decisioning, distributed training frameworks, and large-scale AI infrastructure.
