ML Ops Engineer

Summary

Join Canibuild, a fast-growing SaaS platform, as our MLOps Engineer. You will build and maintain our AI/ML infrastructure, ensuring efficient model training, deployment, and monitoring. This role involves automating ML workflows, optimizing AI operations, and improving model reliability. You will collaborate with the ML team and IT/Engineering to streamline AI deployment. We offer flexible remote work and career development opportunities within a supportive global team. A competitive salary is also provided.

Requirements

• 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)
• Experience 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

Responsibilities

• Implement CI/CD pipelines for model training, testing, and deployment
• Develop scalable ML infrastructure to ensure reliable AI model performance
• Automate model retraining, versioning, and monitoring using MLflow, Kubeflow, or Airflow
• Deploy ML models on cloud platforms (AWS, Azure, GCP) and manage Kubernetes/Docker environments
• Assist in optimizing data pipelines and integrating AI models with production systems
• Ensure AI deployments adhere to security, governance, and compliance standards

Benefits

• Flexible remote work opportunities with career development opportunities
• Engagement with a supportive and collaborative global team
• Competitive market based salary