The MLOps Engineer will establish and maintain AI/ML infrastructure, automate ML workflows, and enhance model reliability. Responsibilities include implementing CI/CD pipelines, deploying models, optimizing cloud environments, and ensuring compliance. Collaboration with the ML and IT teams is essential to streamline AI deployments.
About Us
Canibuild automates the residential construction industry’s design, approval, and sales processes, allowing clients to answer 'Can I build this on this plot of land?' instantly. As a fast-growing SaaS platform backed by Australia’s largest hedge fund, we serve clients across Australia, New Zealand, Canada, and the US.
Job Overview
The MLOps Engineer will establish and maintain AI/ML infrastructure, ensuring models are efficiently trained, deployed, and monitored. This role focuses on automating ML workflows, optimizing AI operations, and improving model reliability. The MLOps Engineer will work closely with the ML team and IT/Engineering to streamline AI deployment at Canibuild.
Key Responsibilities
- CI/CD for ML: Implement CI/CD pipelines for model training, testing, and deployment.
- Model Deployment & Monitoring: Develop scalable ML infrastructure to ensure reliable AI model performance.
- Automation & Infrastructure Optimization: Automate model retraining, versioning, and monitoring using MLflow, Kubeflow, or Airflow.
- Cloud & Containerization: Deploy ML models on cloud platforms (AWS, Azure, GCP) and manage Kubernetes/Docker environments.
- Data Engineering Support: Assist in optimizing data pipelines and integrating AI models with production systems.
- Security & Compliance: Ensure AI deployments adhere to security, governance, and compliance standards.
- Bachelor’s/Master’s in Computer Science, AI, or related field
- 4+ years in MLOps, AI infrastructure, or DevOps
- Strong expertise in CI/CD tools for ML (e.g., MLflow, Kubeflow, Airflow)
- xperience with cloud ML services (AWS SageMaker, Google Vertex AI, Azure ML)
- Proficiency in container orchestration (Docker, Kubernetes).
- Understanding of AI model monitoring, logging, and explainability frameworks
- Flexible remote work opportunities with career development opportunities
- Engagement with a supportive and collaborative global team
- Competitive market based salary
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