AI Engineer

AI Lead – AI in UPI 1. About the Job We are looking for an AI Leader to drive AI orchestration initiatives for Neuron, our internal code-assist platform. This role involves building intelligent systems that accelerate software development through AI-powered code generation, validation, and optimization. 2. Your Team's Impact Your team will transform how NPCI builds and maintains applications by leveraging AI for faster, more reliable code delivery. You will enable developers to work smarter, reduce errors, and improve productivity across mission-critical payment systems. 3. What You'll Do • Architect and implement AI orchestration workflows for code assist. • Develop models for code generation, bug detection, and automated testing. • Integrate AI tools with CI/CD pipelines and developer environments. • Ensure scalability, security, and compliance in AI-driven development processes. 4. What We're Looking For • Experience: 6–9 years in AI/ML and software engineering leadership. Skills: • Proficiency in Python, JavaScript, TypeScript, and AI frameworks (OpenAI, Hugging Face). • Experience with orchestration tools (Airflow, Kubernetes) and microservices. • Strong knowledge of DevOps, CI/CD, and secure coding practices. 5. What's in it for You • Lead AI-driven transformation in software development for India’s payment backbone. • Work on innovative AI solutions impacting millions of transactions. • Access to cutting-edge AI technologies and research collaborations. • Attractive compensation and leadership career path. 6. Company Overview NPCI is the driving force behind India’s digital payment revolution. We innovate at scale to deliver secure, reliable, and inclusive payment solutions. Join us to shape the future of AI in fintech. Technology Spectrum: • Python (core for AI/ML development) • Java Basics • TensorFlow – Deep learning models • PyTorch – Neural networks and advanced ML • Hugging Face – NLP and transformer-based models • OpenAI APIs – Generative AI for code assist and conversational AI Python Libraries: • Scikit-learn – Classical ML algorithms • Pandas & NumPy – Data manipulation and numerical computing • Matplotlib & Seaborn – Visualization • NetworkX – Graph analytics for mule detection • FastAPI / Flask – Serving AI models as APIs Generative AI (GenAI): • LLMs (Large Language Models) – GPT-based models for text/code generation • Multi-Modal AI – Vision + Text + Speech models for advanced fraud detection and user behavior analysis • Agentic AI – Autonomous agents for workflow orchestration and decision-making in fraud prevention and code assist Data Platforms: Kafka – Real-time streaming • Kubernetes – Container orchestration for AI workloads • Docker – Containerization for portability • AI Orchestration – Automated workflows for code assist • Code Generation & Validation – Generative AI for developer productivity