Role Overview You will be part of NPCI’s Market Innovation team, working at the intersection of advanced machine learning, deep learning, graph AI, and Generative AI to build next-generation intelligent systems for India’s digital payments ecosystem. This role focuses on solving India-scale problems such as fraud detection, mule/AML risk modeling, transaction intelligence, and conversational AI, using both classical ML and cutting-edge AI architectures (LLMs, GNNs, Transformers, Agentic AI systems). You will design end-to-end AI systems—from problem formulation, feature engineering, and model development to GPU-accelerated optimization and production deployment, ensuring low latency, scalability, and robustness. The role offers a unique opportunity to work on: • Graph-based fraud detection systems • Agentic AI & LLM-powered platforms (RAG, MCP, workflows) • GPU/CUDA optimized AI pipelines • Privacy-preserving and federated AI systems You will collaborate with top academic institutions (IITs/IISc) and cross-functional teams to push the boundaries of applied AI in financial systems. Job Details • Job Title: Data Scientist – AI Engineer • Division: NPCI Data Analytics – Market Innovation • Education: B.Tech / M.Tech / MSc / MCA (PhD preferred) in CS, AI, DS, Mathematics or related field • Employment Type: Full-time • Location: Hyderabad • Role Type: Permanent Key Responsibilities Machine Learning & Advanced Modeling • Develop and deploy ML/DL models (Logistic Regression, RF, XGBoost, NN, CNN, Transformers, GANs) • Build models for fraud detection, AML, anomaly detection, transaction intelligence • Work on imbalanced datasets using advanced sampling and cost-sensitive learning Graph AI & Advanced Systems • Design Graph AI models: GNN, GCN, GAT, temporal graph networks • Apply network analytics for fraud rings, mule detection, behavioral risk signals Generative AI & Agentic Systems • Build LLM-powered applications (chatbots, complaint intelligence, document analysis) • Implement: • RAG pipelines • Agentic workflows & MCP (Model Context Protocols) • Prompt engineering & LLM fine-tuning Feature Engineering & Data Science • Perform EDA, feature engineering (temporal, behavioral, aggregated features) • Work with structured, semi-structured, and unstructured data Model Optimization & GPU Acceleration • Optimize models for: • Latency & throughput • GPU performance (CUDA-based optimization) • Use libraries such as: • RAPIDS, cuDF, cuML, cuGraph, PyTorch Geometric Evaluation & Experimentation • Design custom loss functions (weighted BCE, cost-sensitive) • Apply business-aligned metrics: • Precision@K, Recall, ROC-AUC, PR-AUC • Use robust validation techniques (cross-validation, time-based splits) Deployment & Production Systems • Integrate models into batch and real-time production systems • Design scalable ML pipelines & APIs • Monitor: • Model drift • Performance stability • Business impact Collaboration & Research • Work with data engineers, product teams, and business stakeholders • Contribute to research, innovation, and academic collaborations • Stay updated on latest AI advancements (LLMs, Graph AI, Federated Learning) Requirements Required Technical Skills Core ML & Data Science • Strong in: • Supervised & unsupervised learning • Statistical modeling (Logistic Regression, DA) • Tree models (RF, XGBoost, LightGBM) • Deep Learning: • NN, CNN, Transformers, GANs Generative AI & LLM Stack • Hands-on experience with: • LLMs (OpenAI, open-source models) • Prompt engineering, fine-tuning • RAG pipelines & vector databases • Agent frameworks & MCPs Graph AI • Experience with: • GNN, GCN, GAT • Graph-based fraud detection • Network analytics Programming & Tools • Strong proficiency in: • Python (NumPy, Pandas, scikit-learn) • SQL (large-scale data processing) • Frameworks: • PyTorch / TensorFlow • PyTorch Geometric Key Skills and Experience Required • Strong foundation in: • Mathematics, probability, statistics • Data structures & algorithms • Expertise in: • Feature engineering & model evaluation • Handling large-scale datasets • Experience with: • Imbalanced datasets & sampling techniques • Custom loss functions & business metrics • Knowledge of: • Model deployment & production pipelines • Model monitoring & performance tracking • Strong: • Problem-solving ability • Communication & stakeholder management • Ability to translate business problems into scalable AI systems Good-to-Have Skills & Experience • Experience in: • Payments / fintech / banking domain • Fraud detection, AML, mule detection systems • Exposure to: • Graph analytics on transactional data • Federated learning & privacy-preserving AI • Real-time streaming systems • Experience with: • Cloud platforms (AWS/GCP/Azure) • ML pipelines & MLOps frameworks • Research experience: • Publications in ML/AI conferences or journals • Ability to: • Design AI models inspired by mathematics/physics principles