## About the Role

The DevOps Engineer will be responsible for developing and maintaining AI applications, implementing CI/CD pipelines, and integrating AI and machine learning capabilities into products and workflows. This role bridges software development, infrastructure operations and applied AI.

## Key Responsibilities

- Build and maintain DevOps infrastructure for LLM inference and AI-powered products, including model serving, scaling and monitoring

- Integrate AI and machine learning models into applications and operational workflows, including leveraging large language models (LLMs), computer vision or predictive analytics as required

- Design and implement AI evaluation frameworks to measure model performance, accuracy and reliability across production systems

- Design, implement and maintain CI/CD pipelines to support automated software delivery

- Develop and manage cloud infrastructure using platforms such as AWS, Azure or Google Cloud

- Implement Infrastructure as Code (IaC) using tools such as Terraform, CloudFormation or similar

- Monitor system performance and reliability and implement improvements to ensure high availability

- Automate system administration tasks using scripting languages such as Python, Bash or PowerShell

- Manage containerised environments using Docker and Kubernetes

- Implement security best practices across infrastructure, applications and deployment pipelines

- Troubleshoot system issues and provide technical support across production environments

- Maintain documentation for infrastructure architecture, application design and deployment processes

## Skills and Experience

### Mandatory

- Bachelor degree in Computer Science, Software Engineering or related field (or equivalent experience)

- Minimum 5 years relevant experience in development, DevOps, or infrastructure engineering

- Demonstrated experience with AI/ML technologies, including integration of machine learning models, LLM APIs (e.g. OpenAI, Azure AI, AWS SageMaker) or AI-powered tooling into production applications

- Experience with AI evaluation methodologies, including designing and running evals to assess model performance, accuracy and safety

- Experience with DevOps practices for LLM inference and AI product delivery, including model deployment, serving infrastructure and performance monitoring

- Strong development skills with experience backend languages (Python, Node.js, Java or similar)

- Strong experience with cloud platforms (AWS, Azure or GCP)

- Experience with CI/CD tools such as Jenkins, GitLab CI, GitHub Actions or similar

- Experience with containerisation technologies such as Docker and Kubernetes

- Knowledge of Infrastructure as Code tools such as Terraform or CloudFormation

### Desirable

- Experience building or fine-tuning ML models using frameworks such as TensorFlow, PyTorch or scikit-learn

- Experience with data pipelines and tools such as Apache Airflow, Spark or similar

- Experience with Linux systems administration and automation scripting

- Strong understanding of system security, networking and monitoring tools

- Excellent problem-solving skills and ability to work collaboratively within a technical team

## What We Offer

- Competitive salary package of $140,000 – $160,000 plus superannuation (depending on experience)

- Opportunity to work with modern cloud technologies, DevOps tools and cutting-edge AI solutions

- Ongoing training and professional development opportunities

- Exposure to a wide range of IT infrastructure, cybersecurity and digital transformation projects

## How to Apply

If you are passionate about automation, cloud technologies and AI-driven innovation, we would love to hear from you.