Agentic AI Data Scientist

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 Title: Agentic AI Data Scientist
  
• Division: NPCI Data Analytics – Market Innovation
  
• Education: B.Tech / M.Tech / MSc / MCA (PhD preferred) in CS, AI, DS, Mathematics or related field
  
• Experience Required: 3 to 6 Years
  
• Employment Type: Full-time
  
• Location: Mumbai / Hyderabad
  
• Role Type: Permanent
  

• 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
  

• Design Graph AI models: GNN, GCN, GAT, temporal graph networks
  
• Apply network analytics for fraud rings, mule detection, behavioral risk signals
  

• Build LLM-powered applications (chatbots, complaint intelligence, document analysis)
  
• Implement:
  
  • RAG pipelines
    
  • Agentic workflows & MCP (Model Context Protocols)
    
  • Prompt engineering & LLM fine-tuning
    

• Perform EDA, feature engineering (temporal, behavioral, aggregated features)
  
• Work with structured, semi-structured, and unstructured data
  

• Optimize models for:
  
  • Latency & throughput
    
  • GPU performance (CUDA-based optimization)
    
• Use libraries such as:
  
  • RAPIDS, cuDF, cuML, cuGraph, PyTorch Geometric
    

• 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)
  

• Integrate models into batch and real-time production systems
  
• Design scalable ML pipelines & APIs
  
• Monitor:
  
  • Model drift
    
  • Performance stability
    
  • Business impact
    

• 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)
  

• Strong in:
  
  • Supervised & unsupervised learning
    
  • Statistical modeling (Logistic Regression, DA)
    
  • Tree models (RF, XGBoost, LightGBM)
    
• Deep Learning:
  
  • NN, CNN, Transformers, GANs
    

• Hands-on experience with:
  
  • LLMs (OpenAI, open-source models)
    
  • Prompt engineering, fine-tuning
    
  • RAG pipelines & vector databases
    
  • Agent frameworks & MCPs
    

• Experience with:
  
  • GNN, GCN, GAT
    
  • Graph-based fraud detection
    
  • Network analytics
    

• Strong proficiency in:
  
  • Python (NumPy, Pandas, scikit-learn)
    
  • SQL (large-scale data processing)
    
• Frameworks:
  
  • PyTorch / TensorFlow
    
  • PyTorch Geometric
    

• 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
  

• 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