Remote MLOps Lead AI Engineer

Boardroom Appointments; Cape Town; Remote; ZAR 1,200,000 - 2,100,000; Full time

Benefits offered by this job

Competitive salary with incentives

Fully remote work environment

Medical, dental, vision insurance

Professional development opportunities

Access to cutting-edge MLOps tools

Job summary

Boardroom Appointments is seeking a Lead AI Engineer with a specialty in MLOps to drive remote engineering efforts and build scalable pipelines for model training, deployment, and monitoring. You will collaborate with distributed teams to ensure reliable, efficient AI systems and establish a high-performance MLOps framework.

The role emphasizes remote collaboration, cloud-native infrastructure, and leadership in the AI/ML lifecycle, offering a competitive compensation package and comprehensive

Qualifications

• Master's degree or Ph.D. in Computer Science, Engineering, or a related field.
• 5+ years of software engineering experience with a focus on MLOps.
• Proficiency with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Experience with ML workflow orchestration tools (Kubeflow, MLflow, Airflow).
• Strong understanding of machine learning concepts and model lifecycle management.

Responsibilities

• Design, build, and maintain scalable MLOps infrastructure and CI/CD pipelines for machine learning models.
• Develop and implement strategies for model monitoring, versioning, and retraining.
• Automate the deployment and management of ML models into production environments.
• Collaborate with data scientists and software engineers to optimize the end-to-end ML lifecycle.
• Ensure the reliability, scalability, and security of deployed AI systems.

Skills

MLOps

CI/CD

ML lifecycle

Cloud-native

Collaborative

Education

Masters or PhD in CS/Engineering

Tools

AWS

Azure

GCP

Docker

Kubernetes

Kubeflow

MLflow

Airflow

Job description

Boardroom Appointments is seeking a Lead AI Engineer with a specialty in MLOps to drive remote engineering efforts and build scalable pipelines for model training, deployment, and monitoring. You will collaborate with distributed teams to ensure reliable, efficient AI systems and establish a high-performance MLOps framework.

The role emphasizes remote collaboration, cloud-native infrastructure, and leadership in the AI/ML lifecycle, offering a competitive compensation package and comprehensive