Multiskilled Operator

Fraser Alexander (Pty) Ltd; South Africa; Remote; ZAR 700,000 - 1,100,000; Full time

Benefits offered by this job

Competitive salary

Health coverage

Fully remote

Professional development

AI projects

Paid time off

Job summary

Fraser Alexander (Pty) Ltd seeks an experienced Remote MLOps Engineer to design and manage scalable ML pipelines in production. You will automate training, deployment, monitoring, and retraining while collaborating with data scientists and software engineers across a global, remote-first team.

The role emphasizes Docker/Kubernetes, cloud ML services, and robust CI/CD practices. A strong Python background and effective communication in a distributed environment are essential.

Qualifications

• Bachelor's or Master’s degree in Computer Science, Software Engineering, or related field.
• Proven experience in MLOps, DevOps, or related role focused on ML lifecycles.
• Strong programming skills in Python and scripting languages.
• Proficiency with cloud platforms (AWS, Azure, GCP) and their ML services.
• Experience with Docker and Kubernetes.
• Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Understanding of CI/CD principles and tools (Jenkins, GitLab CI, GitHub Actions).
• Experience with monitoring tools for ML systems.
• Strong problem-solving and remote collaboration skills.

Responsibilities

• Design, build, and maintain scalable MLOps pipelines for CI/CD of ML models.
• Implement tools for training, versioning, deployment, monitoring, and retraining.
• Collaborate with data scientists to operationalize models from development to production.
• Develop infrastructure for ML model serving using containerization and orchestration.
• Implement monitoring for ML models in production, tracking performance and drift.
• Automate data and model validation and testing procedures.
• Manage cloud-based ML infrastructure across AWS, Azure, and GCP.
• Ensure security, compliance, and cost-efficiency of deployments.
• Stay updated with latest MLOps tools and best practices.
• Provide guidance to data science and engineering teams.

Skills

Python

Cloud platforms

Docker

Kubernetes

CI/CD

ML frameworks

Jenkins

GitHub Actions

Remote collaboration

Education

Bachelor's or Master's in CS/SE

Tools

Docker

Kubernetes

TensorFlow

PyTorch

Scikit-learn

Jenkins

GitHub Actions

AWS

Azure

GCP

Job description

Remote Machine Learning Operations (MLOps) Engineer

About the Role Our client is seeking an experienced Machine Learning Operations (MLOps) Engineer to join their fully remote team. This role is critical for building, automating, and managing the lifecycle of machine learning models in production environments. You will be responsible for establishing robust MLOps pipelines, ensuring the smooth deployment, monitoring, and maintenance of ML models across various platforms. This is a hands-on technical role for an individual passionate about bridging the gap between machine learning development and operational excellence. As a remote-first employee, you will have the opportunity to work flexibly while collaborating closely with data scientists and software engineers globally. Your expertise in DevOps principles applied to machine learning will be invaluable in accelerating our client's AI initiatives and ensuring the reliability and scalability of their ML systems.

Key Responsibilities

• Design, build, and maintain scalable and automated MLOps pipelines for CI/CD of machine learning models.
• Implement tools and processes for model training, versioning, deployment, monitoring, and retraining.
• Collaborate with data scientists to operationalize machine learning models, ensuring their smooth transition from development to production.
• Develop and manage infrastructure for ML model serving, including containerization (Docker) and orchestration (Kubernetes).
• Implement robust monitoring solutions for ML models in production, tracking performance, drift, and potential issues.
• Automate data validation, model validation, and testing procedures.
• Manage and optimize cloud-based ML infrastructure (AWS, Azure, GCP).
• Ensure security, compliance, and cost-efficiency of ML deployments.
• Stay updated with the latest MLOps tools, technologies, and best practices.
• Provide technical guidance and support to data science and engineering teams on MLOps best practices.

Requirements

• Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
• Proven experience in MLOps, DevOps, or a related role focused on automating and managing ML lifecycles.
• Strong programming skills in Python and experience with scripting languages.
• Proficiency with cloud platforms (AWS, Azure, GCP) and their ML services.
• Experience with containerization technologies (Docker) and orchestration tools (Kubernetes).
• Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn) and ML libraries.
• Understanding of CI/CD principles and tools (e.g., Jenkins, GitLab CI, GitHub Actions).
• Experience with monitoring tools and techniques for ML systems.
• Strong problem-solving and troubleshooting skills.
• Excellent communication and collaboration abilities for a remote team environment.

Benefits

• Competitive salary package.
• Comprehensive health, dental, and vision insurance.
• Fully remote work arrangement, offering significant flexibility.
• Opportunities for continuous professional development and learning.
• Chance to work on impactful, cutting-edge AI projects.
• A collaborative and inclusive remote team culture.
• Generous paid time off.