GenAI DevOps Engineer

The Opportunity

is a rapidly evolving enterprise AI productivity platform, and we are looking for a skilled GenAI DevOps Engineer to join our team. Our products,  and Rememberizer, are currently deployed on Amazon Web Services (AWS) and we are transitioning to custom hardware deployments using Kubernetes. This role will bridge the gap between development and operations, ensuring smooth deployment, scalability, and maintenance of our AI-driven platforms.

As a GenAI DevOps Engineer, you will be instrumental in creating a robust and efficient infrastructure that supports our generative AI applications. You will work closely with our development team to integrate continuous integration/continuous deployment (CI/CD) pipelines, automate deployment processes, and manage cloud and on-premises resources.

This role offers an exceptional opportunity for growth as we continue to advance our venture, providing a wealth of enterprise-ready tools and capabilities to our users.

Responsibilities:

- Infrastructure Management: Design, provision, and manage cloud and on-premises infrastructure using Kubernetes, AWS, and custom hardware.

- CI/CD Pipeline Implementation: Develop and maintain CI/CD pipelines to automate the building, testing, and deployment of generative AI applications.

- System Administration: Deploy and maintain servers, storage, and networking resources. Manage system patching, security monitoring, and backups.

- Configuration Management: Utilize tools such as Ansible, Puppet, or Chef to automate system administration tasks and manage configurations.

- Containerization: Implement and manage container orchestration using Docker and Kubernetes to ensure scalable and reliable deployments.

- Collaboration and Communication: Work closely with development, QA, and product teams to ensure seamless integration and deployment of AI models and applications.

- DevOps Advocacy: Promote and educate best practices in DevOps within the organization, helping to foster a collaborative and efficient engineering culture.

- Automation: Develop and maintain automation scripts and tools to streamline operations and reduce manual intervention.

- Security: Integrate security best practices into the DevOps pipeline and ensure compliance with organizational security standards.

- Database Management: Manage and optimize large-scale databases, particularly PostgreSQL and pg_vector, as well as vector databases, ensuring high performance, scalability, and reliability. Implement database performance tuning, indexing, and query optimization.