Converter Operator

Remote Machine Learning Operations Engineer (MLOps)

Our client is seeking a highly skilled Remote Machine Learning Operations Engineer (MLOps) to join their innovative, distributed technology team. This role is essential for building and maintaining the infrastructure and pipelines that support the machine learning lifecycle, from experimentation to production deployment and monitoring. You will ensure the reliability, scalability, and efficiency of our ML systems. Working remotely, you will collaborate with data scientists and software engineers to streamline the deployment and operationalization of ML models across various cloud environments.

About the Role

Our client is seeking a highly skilled Remote Machine Learning Operations Engineer (MLOps) to join their innovative, distributed technology team. This role is essential for building and maintaining the infrastructure and pipelines that support the machine learning lifecycle, from experimentation to production deployment and monitoring. You will ensure the reliability, scalability, and efficiency of our ML systems. Working remotely, you will collaborate with data scientists and software engineers to streamline the deployment and operationalization of ML models across various cloud environments.

Key Responsibilities

• Design, build, and maintain robust CI/CD pipelines for machine learning models.
• Implement and manage infrastructure for ML model training, deployment, and serving, leveraging cloud platforms.
• Develop and automate monitoring systems for ML model performance, drift, and system health.
• Collaborate with data scientists to optimize models for production readiness and scalability.
• Implement version control strategies for data, code, and models.
• Troubleshoot and resolve issues related to ML system performance and reliability.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field; Master's preferred.
• 3-5 years of experience in MLOps, DevOps, or site reliability engineering with a focus on ML.
• Proficiency in Python and scripting languages, with experience in cloud platforms (AWS, Azure, GCP).
• Strong understanding of containerization technologies (Docker, Kubernetes).
• Experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Familiarity with data pipelines, monitoring tools, and CI/CD principles.

Benefits

• Competitive salary tailored for remote roles.
• Fully remote work arrangement with flexible hours.
• Comprehensive health, dental, and vision benefits.
• Generous paid time off and annual leave.
• Opportunities for continuous learning and professional development in cutting-edge MLOps practices.

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