Remote ML Ops Engineer (Remote)

Description:

• 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 include implementing CI/CD pipelines for model training, testing, and deployment.
• The engineer will develop scalable ML infrastructure to ensure reliable AI model performance.
• Automation of model retraining, versioning, and monitoring will be done using MLflow, Kubeflow, or Airflow.
• The role involves deploying ML models on cloud platforms (AWS, Azure, GCP) and managing Kubernetes/Docker environments.
• The engineer will assist in optimizing data pipelines and integrating AI models with production systems.
• Ensuring AI deployments adhere to security, governance, and compliance standards is also a key responsibility.

Requirements:

• A Bachelor’s or Master’s degree in Computer Science, AI, or a related field is required.
• The candidate must have 4 or more years of experience in MLOps, AI infrastructure, or DevOps.
• Strong expertise in CI/CD tools for ML, such as MLflow, Kubeflow, or Airflow, is necessary.
• Experience with cloud ML services, including AWS SageMaker, Google Vertex AI, or Azure ML, is required.
• Proficiency in container orchestration tools like Docker and Kubernetes is essential.
• An understanding of AI model monitoring, logging, and explainability frameworks is needed.

Benefits:

• The position offers flexible remote work opportunities along with career development opportunities.
• Employees will engage with a supportive and collaborative global team.
• A competitive market-based salary is provided.