Machine Learning Operations (MLOps) Engineer

Placements24; Paarl; Teletrabalho; ZAR 850 000 - 1 200 000; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Competitive salary

Health benefits

Paid time off

Professional development budget (confs

Remote-first culture

Home office stipend

Resumo da oferta

Placements24 is hiring an experienced MLOps Engineer for a fully remote team. You will bridge ML development and production deployment, building scalable infrastructure, automated pipelines, and monitoring systems.

You will work with data scientists and software engineers to ensure reliable model delivery and production readiness in a remote, collaborative environment.

Qualificações

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• Minimum of 4 years in software engineering or DevOps, with at least 2 years focused on MLOps.
• Strong proficiency in Python and experience with ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Hands-on experience with cloud platforms (AWS, Azure, GCP) and their ML services.
• Expertise in containerization (Docker) and Kubernetes.
• Experience with CI/CD tools (Jenkins, GitLab CI, Argo CD) and infrastructure as code (Terraform, Ansible).
• Solid understanding of monitoring/logging tools (Prometheus, Grafana, ELK).
• Familiarity with ML concepts and lifecycle; strong remote collaboration skills.

Responsabilidades

• Develop and manage automated ML pipelines for training, evaluation, deployment, and monitoring of ML models.
• Implement and maintain infrastructure for ML model deployment, including Docker and Kubernetes.
• Establish and manage CI/CD practices for ML projects.
• Monitor performance and health of ML models in production and address issues.
• Collaborate with data scientists to streamline the model development lifecycle.
• Work with software engineers to integrate ML models into applications.
• Implement tools for model versioning, experiment tracking, and data lineage.
• Ensure scalability, reliability, and security of the MLOps infrastructure.
• Stay up-to-date with the latest MLOps tools and best practices.
• Contribute to design and architecture of the ML platform.

Conhecimentos

Python programming

Problem solving

Remote collaboration

ML concepts

Formação académica

Bachelor's or Master's in Computer Science or related field

Ferramentas

Docker

Kubernetes

Terraform

Ansible

Jenkins

GitLab CI

Argo CD

Airflow

Prometheus

Grafana

ELK

Descrição da oferta de emprego

About the Role

Our client, a leader in AI-driven solutions, is seeking an experienced Machine Learning Operations (MLOps) Engineer to join their fully remote team. In this critical role, you will be responsible for bridging the gap between machine learning development and reliable production deployment. You will design, build, and maintain the infrastructure and pipelines necessary to deploy, monitor, and manage machine learning models efficiently and at scale. This remote-first position demands a strong understanding of both software engineering and machine learning principles, with a focus on automation, scalability, and operational excellence. You will work closely with data scientists and engineers to ensure our AI models are delivered reliably and perform optimally in production environments. We are dedicated to creating a highly collaborative and productive remote workspace.

Key Responsibilities

• Develop and manage automated ML pipelines for training, evaluation, deployment, and monitoring of machine learning models.
• Implement and maintain infrastructure for ML model deployment, including containerization (Docker) and orchestration (Kubernetes).
• Establish and manage CI/CD practices for machine learning projects.
• Monitor the performance and health of ML models in production, identifying and addressing issues proactively.
• Collaborate with data scientists to streamline the model development lifecycle and ensure models are production-ready.
• Work with software engineers to integrate ML models into existing applications and services.
• Implement tools and processes for model versioning, experiment tracking, and data lineage.
• Ensure scalability, reliability, and security of the MLOps infrastructure.
• Stay up-to-date with the latest MLOps tools, technologies, and best practices.
• Contribute to the design and architecture of our ML platform.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• Minimum of 4 years of experience in software engineering or DevOps, with at least 2 years focused on MLOps.
• Strong proficiency in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Hands-on experience with cloud platforms (AWS, Azure, GCP) and their ML services.
• Expertise in containerization technologies (Docker) and container orchestration (Kubernetes).
• Experience with CI/CD tools (e.g., Jenkins, GitLab CI, Argo CD) and infrastructure as code (e.g., Terraform, Ansible).
• Solid understanding of monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack).
• Familiarity with ML concepts and the ML lifecycle.
• Excellent problem-solving and communication skills, with a proven ability to work effectively in a remote, collaborative team.
• Experience with data pipeline tools (e.g., Airflow) is a plus.

Benefits

• This is a fully remote role offering a highly competitive salary, comprehensive health benefits, generous paid time off, and a dedicated budget for professional development, including conferences, training, and certifications.
• Our client provides a dynamic and innovative work environment where you can make a significant impact on the advancement of AI technologies.
• Enjoy the flexibility and autonomy of a remote-first culture, working with a diverse and talented global team.
• We also offer stipends for home office equipment and support for maintaining a productive remote workspace.
• This is a fantastic opportunity to advance your career in a critical and high-demand field, contributing from Paarl, Western Cape, or any other preferred location.