Senior MLOps Engineer

Salla; Saudi Arabia; On-site; SAR 120,000 - 160,000; Full time

Job summary

A technology company in Saudi Arabia is seeking a Senior MLOps Engineer to enhance their machine learning lifecycle. You will design and manage scalable MLOps pipelines, oversee model training and deployment workflows, and collaborate with data scientists to ensure operational efficiency. The ideal candidate possesses strong problem-solving skills and proficiency in cloud platforms like AWS, GCP, or Azure.

Qualifications

• Experience in building and maintaining scalable MLOps pipelines.
• Proficient in cloud-based ML resource management.
• Familiar with continuous integration and deployment frameworks.

Responsibilities

• Design and maintain MLOps pipelines for collaboration.
• Implement workflows for model training, deployment, and monitoring.
• Monitor model performance and optimize as needed.

Skills

MLOps pipeline design

Cloud platforms (AWS, GCP, Azure)

Model optimization

Problem-solving

Communication skills

Job description

Salla is on the lookout for a talented Senior MLOps Engineer to help streamline our machine learning lifecycle by implementing best practices and innovative solutions. In this role, you will work closely with data scientists, software engineers, and stakeholders to deploy robust machine learning models and maintain efficient operations.

Responsibilities:

• Design, build, and maintain scalable MLOps pipelines that foster collaboration between data scientists and engineering teams.
• Implement and manage workflows for model training, validation, deployment, and monitoring.
• Utilize cloud-based platforms (AWS, GCP, or Azure) to provision and manage machine learning resources.
• Develop tools and frameworks that assist in continuous integration and deployment of machine learning models.
• Collaborate with data scientists to understand model requirements and assist in feature engineering and selection.
• Monitor model performance in production, identify issues, and provide recommendations for model optimization.
• Establish best practices for versioning, testing, and documenting machine learning models.
• Stay updated with the latest developments in MLOps tools and methodologies.
• Strong communication skills to relay technical concepts to non-technical stakeholders.
• Excellent problem-solving skills and a proactive approach to addressing challenges.