AI Principal Engineer

Role Overview

• 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.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or a related field (minimum)
• Expertise in Software Development.
• 7+ years of relevant experience, ideally with a focus on AI model integration.
• Strong knowledge of GenAI/LLMs, including model selection, tuning, and embedding strategies.
• Experience developing APIs enabling communication between front-end applications and AI systems.
• Working knowledge of Docker and Kubernetes.
• Familiarity with cloud platforms (AWS, GCP, Azure) for scalable AI deployment.
• Experience with vector databases and their integration with LLM-driven applications.
• Familiarity with SQL and NoSQL databases, as well as caching solutions (e.g., Redis).
• Experience with CI/CD pipelines, Git, and DevOps practices.
• Excellent command of English AND Polish.

Tech Stack

• AWS
• Azure
• Cloud
• Distributed Systems
• Docker
• Google Cloud Platform
• Java
• Kubernetes
• Microservices
• NoSQL
• Redis
• SQL

Benefits

• The option to work from the office or 100% remotely
• Opportunity to work in a diverse, international and cross-functional environment, along with leading experts.
• Fulfilling careers with a range of benefits for employees and invests in providing training opportunities for their development.
• Involvement in technology monitoring and choices
• Your impact will be visible instantly and you will be making a difference in our users lives
• Participation in building new Centre of Excellence at InPost