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.

Lead Data Scientist / AI/ML Engineer