Senior Data Scientist

National Payment Corporation of India (NPCI) is hiring!

Senior Data Science (AI/ML Systems)

Location: Hyderabad

Role Type: Permanent

Experience: 11 - 15 years

The Opportunity

At NPCI, you will drive the design and deployment of nextgeneration AI and data science systems that power Indias digital payments ecosystem at population scale. This role goes beyond traditional modelingrequiring deep hands-on expertise in building end-to-end AI/ML pipelines, production-grade platforms, and scalable distributed systems.

You will work with advanced machine learning, deep learning, and LLM-based architectures while leading high-impact initiatives across product, engineering, and regulatory domains. This is an opportunity to build AI systems that operate at national scale with high performance, low latency, and strong reliability.

Job Summary

We are seeking a Senior Data Scientist who brings strong hands-on technical depth in AI/ML along with the ability to architect, scale, and lead complex analytical systems. The ideal candidate is an expert in machine learning, deep learning, and large-scale system designcapable of taking AI solutions from concept to production with high engineering rigor.

Key Responsibilities

AI/ML System Design & Development

• Design, develop, and deploy production-grade machine learning and deep learning systems (TensorFlow, PyTorch, Keras).
• Build and operationalize supervised and unsupervised learning models, including classical statistical models, tree-based methods, neural networks, and advanced DL architectures.
• Architect and optimize end-to-end ML pipelines covering data ingestion, feature engineering, training, serving, scaling, and monitoring.

Large-Scale & Distributed Systems

• Work with big data ecosystems and distributed computing technologies to handle extremely large datasets at high throughput.
• Design systems for high availability, ultra-low latency, observability, and fault tolerance.

MLOps & Deployment

• Implement CI/CD pipelines for ML workflows.
• Work with Docker, Kubernetes, container orchestration, GPU clusters, and scalable microservice architectures.
• Integrate platform components such as MinIO, Trino, Feature Stores, and distributed caching.

Advanced AI & LLMs

• Apply LLMs, transformer models, and agentic AI systems where relevant.
• Explore federated AI architectures for privacy-preserving ML.

Technical Leadership & Collaboration

• Mentor data scientists and ML engineers; lead architectural decisions and code reviews.
• Work closely with Product, Engineering, Business, and Operations teams to deliver enterprise AI solutions.
• Ensure strong alignment with audit, compliance, and regulatory expectations.
• Contribute to NPCIs thought leadership via white papers, conference presentations, and technical publications.

Key Skills & Experience Required

Core Technical Expertise

• Strong foundations in Machine Learning, Deep Learning, Statistics, and Bayesian methods.
• Expert-level proficiency in Python and strong command of SQL.
• Hands-on experience with TensorFlow, PyTorch, and GPU-accelerated computing (CUDA, NVIDIA stack).
• Experience working with large-scale data systems and distributed storage/compute.
• Practical understanding of feature store architectures and ML observability frameworks.

Engineering & System Design

• Strong knowledge of:
• Kubernetes, Docker
• CI/CD for ML (GitOps, DevOps, MLOps)
• MinIO, Trino/Presto, Object Storage, Data Lakes
• Model serving, monitoring, and drift detection

AI at Scale

• Experience designing ML systems optimized for scalability, latency, fault tolerance, performance tuning, and resource management.
• Exposure to federated learning, distributed model training, and agentic AI.

Soft Skills & Leadership

• Excellent problem-solving, analytical thinking, and communication skills.
• Strong ability to translate ambiguous business problems into scalable AI-driven solutions.
• Experience in stakeholder management and cross-functional leadership.

Good-to-Have Skills

• Exposure to digital payments, banking, finance, or regulated environments.
• Experience with Operations Research tools (Google OR, IBM ILOG).
• Published research, white papers, patents, or conference presentations.