About Us

We, at AUI are excited to introduce you to Apollo. Apollo is a breakthrough language model, built with a neuro-symbolic architecture to make conversational agents possible. Apollo enables the native tool use and controllability transformer-based agents lack. Apollo unlocks fine-tuning for agents, allowing continuous evolution from human feedback and ever-improving performance for conversational agents of any kind.

The Role: DevOps Engineer

We're seeking a passionate and skilled DevOps Engineer to design, build, and maintain our infrastructure, ensuring smooth and efficient AI research and deployment.

What You'll Do:

• Architect, design, and implement infrastructures and tools to enable AI research and deployment
• Implement and manage infrastructure stacks for various company applications
• Manage CI/CD pipelines for various applications
• Develop and maintain automation processes for various tasks
• Handle integrations between company systems and third parties
• Administer third-party applications and databases (relational and non-relational)
• Monitor infrastructure resources and applications to ensure 24/7 operation and security

Who We're Looking For:

• An experienced DevOps engineer passionate about building infrastructures for cutting-edge applications
• Someone skilled at creating secure, high-performing, and resilient infrastructures
• A collaborative individual who enjoys working with engineering and data teams to streamline operations and troubleshoot issues

Requirements:

• 3+ years of experience in a similar DevOps role
• Proven experience in UNIX/Linux system administration
• Experience with CI/CD processes and tools (e.g., Jenkins)
• Familiarity with container technologies (Docker, Kubernetes)
• Experience with NoSQL database administration (e.g., MongoDB)
• Proficiency in scripting solutions using Bash or Python
• Knowledge of system monitoring tools such as Datadog
• Strong understanding of cloud security and data security practices

Advantages:

• Experience provisioning and managing Machine Learning infrastructures and tools
• Familiarity with data streaming services like Kafka or Kinesis
• Knowledge of agile software development life cycle