AI Principal Software Engineer (Java) at InPost

We are seeking a skilled and innovative AI Principal Java Software Engineer, experienced in working with Generative AI (GenAI) models, such as Large Language Models (LLMs), and integrating these solutions into business applications. This role combines software engineering responsibilities with deep knowledge of LLMS, APIs, and cloud infrastructure - focused on building modern, AI‑enhanced business applications.    Key Responsibilities  • 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. Model Integration & API Development  • 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. Back-End Development & AI Pipelines  • 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. Infrastructure & Deployment  • 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. LLM and GenAI Ecosystem Expertise  • 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.