• Drive the technical architecture across the domain, with a focus on modernization, scalability and AI integration. • Lead the design and implementation of microservices and cloud-native systems. • Guide the transition from legacy systems to modern distributed systems. • Collaborate with senior stakeholders (EMs, Staff and Principal Engineers, Directors) to align on technology direction. • Champion engineering excellence, fostering a culture of autonomy, accountability, and quality. • Provide mentorship and leadership across engineering teams. • Integrate LLMs and other GenAI models into web applications through efficient API design and implementation. • Build and optimize API endpoints enabling seamless, real-time communication between front-end applications and back-end AI services. • Design and develop secure, scalable, and high-performing Java-based microservices for AI model deployment. • Develop robust back-end systems in Java to support deployment, scalability, and ongoing maintenance of GenAI models. • Build and maintain data pipelines, including preprocessing input data and post-processing model outputs for application use. • Implement best practices for sensitive data handling and maintaining high model performance. • Use Kubernetes and Docker for containerization and orchestration to ensure scalable deployment of AI applications. • Implement CI/CD pipelines for automated testing and delivery of code changes. • Maintain scalable and secure cloud infrastructure using platforms such as Google Cloud Platform or Azure for model training, storage, and deployment. • Utilize vector databases (e.g., Pinecone, Weaviate, Faiss) for embedding management and similarity search. • Work with frameworks supporting model development and deployment, including Hugging Face, LangChain, and OpenAI ecosystem tools. • Optimize and fine-tune LLMs based on specific application needs.