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

- Robust 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

📌 Associate Data Science (Mumbai)
🏢 NPCI
📍 Mumbai