Lead AI/ML Systems Engineer

As a Senior Data Scientist at National Payment Corporation of India (NPCI), you will play a crucial role in driving the design and deployment of next-generation AI and data science systems that underpin India's digital payments ecosystem at a national scale. Your responsibilities will extend beyond traditional modeling as you will need to leverage your expertise in building end-to-end AI/ML pipelines, production-grade platforms, and scalable distributed systems. **Key Responsibilities:** - Design, develop, and deploy production-grade machine learning and deep learning systems using TensorFlow, PyTorch, and Keras. - Build and operationalize supervised and unsupervised learning models, covering classical statistical models, tree-based methods, neural networks, and advanced DL architectures. - Architect and optimize end-to-end ML pipelines encompassing data ingestion, feature engineering, training, serving, scaling, and monitoring. - 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. - Implement CI/CD pipelines for ML workflows. - Utilize Docker, Kubernetes, container orchestration, GPU clusters, and scalable microservice architectures. - Integrate platform components such as MinIO, Trino, Feature Stores, and distributed caching. - Apply LLMs, transformer models, and agentic AI systems as applicable. - Explore federated AI architectures for privacy-preserving ML. - Mentor data scientists and ML engineers; lead architectural decisions and code reviews. - Collaborate closely with Product, Engineering, Business, and Operations teams to deliver enterprise AI solutions. - Ensure alignment with audit, compliance, and regulatory expectations. - Contribute to NPCI's thought leadership through white papers, conference presentations, and technical publications. **Qualifications Required:** - 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). - Practical understanding of feature store architectures and ML observability frameworks. - Strong knowledge of Kubernetes, Docker, CI/CD for ML, MinIO, Trino/Presto, Object Storage, Data Lakes, model serving, monitoring, and drift detection. - 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. - Excellent problem-solving, analytical thinking, and communication skills. - 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. As a Senior Data Scientist at National Payment Corporation of India (NPCI), you will play a crucial role in driving the design and deployment of next-generation AI and data science systems that underpin India's digital payments ecosystem at a national scale. Your responsibilities will extend beyond traditional modeling as you will need to leverage your expertise in building end-to-end AI/ML pipelines, production-grade platforms, and scalable distributed systems. **Key Responsibilities:** - Design, develop, and deploy production-grade machine learning and deep learning systems using TensorFlow, PyTorch, and Keras. - Build and operationalize supervised and unsupervised learning models, covering classical statistical models, tree-based methods, neural networks, and advanced DL architectures. - Architect and optimize end-to-end ML pipelines encompassing data ingestion, feature engineering, training, serving, scaling, and monitoring. - 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. - Implement CI/CD pipelines for ML workflows. - Utilize Docker, Kubernetes, container orchestration, GPU clusters, and scalable microservice architectures. - Integrate platform components such as MinIO, Trino, Feature Stores, and distributed caching. - Apply LLMs, transformer models, and agentic AI systems as applicable. - Explore federated AI architectures for privacy-preserving ML. - Mentor data scientists and ML engineers; lead architectural decisions and code reviews. - Collaborate closely with Product, Engineering, Business, and Operations teams to deliver enterprise AI solutions. - Ensure alignment with audit, compliance, and regulatory expect