AI Engineer, H2O.ai

• Bachelor's, Master's or PhD degree in Computer Science, Engineering, or a related field preferred, but not mandatory
• Proven experience as a Full Stack Engineer or similar role in software development
• Hands-on experience in data engineering, machine learning model development, and full-stack application development
• Contribution on open source projects
• Public track record of AI experience
• Programming Languages: Core proficiency in Python, some experience in JavaScript expected
• AI/ML Frameworks: Proficient in PyTorch, Basic understanding of how to fine-tune deep learning models focusing on LLMs
• Backend: Python, FastAPI, SQL/NoSQL/S3
• Frontend: Experience with frontend frameworks such as React and NextJS
• Deployment: Docker, Kubernetes, CI/CD pipelines, Experience with cloud services, deploying LLMs efficiently
• GenAI tools: OpenAI API endpoints, agents, and integration into applications
• Tools: Ability to utilize AI-assisted coding tools and platforms to accelerate development
• Problem-Solving: Excellent analytical and problem-solving skills with a keen attention to detail. Can quickly adopt new technologies

Desirable

• As an AI Engineer, you will directly collaborate with the Kaggle GM team to bootstrap the development and deployment of transformative AI solutions from concept to market. In this role, you will collaborate on the end-to-end pipeline—data engineering, model development, backend and frontend, system integration, and scalable deployment
• You know how to quickly adopt new technologies and utilize modern tooling for development
• End-to-End Development: Collaborate on the design, development, and implementation of AI applications across the full stack, ensuring seamless integration between frontend, backend, and AI components
• API and Backend Development: Create robust backend services and APIs to support AI functionalities, enabling efficient data flow and integration
• Frontend Development: Develop intuitive user interfaces that provide exceptional user experiences, turning complex AI functionalities into user-friendly applications
• Deployment and Scaling: Deploy AI models and applications using cloud platforms, ensuring scalability, reliability, and performance in production environments
• Tool Utilization: Leverage advanced development tools and AI-assisted coding platforms to enhance productivity and code quality