Senior Machine Learning Engineer - MLOps

Placements24; Mossel Bay; Hybrid; ZAR 900,000 - 1,500,000; Full time

Benefits offered by this job

Salary plus performance-based bonuses

Health insurance

Retirement savings plan

Professional development opportunities

Hybrid work model

Office in Mossel Bay

Job summary

Placements24 in Mossel Bay, Western Cape, is seeking a Senior Machine Learning Engineer with a specialization in MLOps to design and optimize scalable deployment pipelines. The role bridges model development and production, focusing on automation, CI/CD, containerization (Docker/Kubernetes), and cloud platforms (AWS/Azure/GCP).

Hybrid work combines in-office collaboration in Mossel Bay with remote work. You will work closely with data scientists and software engineers to ensure robust,

Qualifications

• Bachelor's or Master's in Computer Science, Engineering or related quantitative field.
• Minimum of 4 years of software or ML engineering experience, with at least 2 years in MLOps.
• Proven experience building and managing CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions).
• Strong understanding of ML concepts and lifecycle; proficient in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn).
• Hands-on experience with Docker and Kubernetes; familiarity with cloud ML services (AWS/Azure/GCP).
• Familiarity with infrastructure-as-code tools (Terraform) is a plus.
• Excellent problem-solving and collaboration abilities.

Responsibilities

• Design, build, and maintain scalable MLOps pipelines for deployment, monitoring, and management of ML models.
• Automate ML workflows: data ingestion, model training, validation, deployment, and monitoring.
• Collaborate with data scientists and software engineers to ensure smooth model production integration.
• Implement infrastructure for model versioning, testing, and rollback strategies.
• Develop and manage CI/CD pipelines specifically for ML projects.
• Monitor model performance in production, identify drift, and plan retraining and redeployment.
• Optimize ML infrastructure for performance, scalability, and cost-effectiveness.
• Work with containerization technologies (Docker, Kubernetes) and cloud platforms (AWS, Azure, GCP).
• Contribute to development of best practices and standards for MLOps.

Skills

Python

ML lifecycle

Communication

CI/CD experience

Education

Bachelor's or Master's in CS/Engineering/related field

Tools

Docker

Kubernetes

TensorFlow

PyTorch

scikit-learn

GitHub Actions

Jenkins

GitLab CI

Terraform

AWS

Azure

GCP

Job description

About the Role

Our client, a dynamic innovator in AI & Emerging Technologies, is seeking a Senior Machine Learning Engineer with a specialization in MLOps to join our forward-thinking team. This role is based in the scenic coastal city of Mossel Bay, Western Cape , and focuses on building and optimizing the infrastructure required to deploy, manage, and scale machine learning models efficiently. You will play a crucial part in bridging the gap between model development and production, ensuring robust and reliable deployment pipelines. This is an excellent opportunity to work with cutting-edge technologies in a collaborative environment that values innovation and continuous improvement. The ideal candidate possesses strong software engineering skills coupled with a deep understanding of machine learning lifecycles. We are committed to fostering professional growth and providing a stimulating work environment. This position follows a hybrid work model, allowing for flexibility and in-office collaboration.

Key Responsibilities

• Design, build, and maintain scalable and robust MLOps pipelines for the deployment, monitoring, and management of machine learning models.
• Automate machine learning workflows, including data ingestion, model training, validation, deployment, and performance monitoring.
• Collaborate closely with data scientists and software engineers to ensure smooth integration of models into production systems.
• Implement infrastructure for model versioning, testing, and rollback strategies.
• Develop and manage CI/CD pipelines specifically for machine learning projects.
• Monitor model performance in production, identify drift, and implement strategies for retraining and re-deployment.
• Optimize ML infrastructure for performance, scalability, and cost-effectiveness.
• Work with containerization technologies (Docker, Kubernetes) and cloud platforms (AWS, Azure, GCP).
• Contribute to the development of best practices and standards for MLOps within the organization.
• Troubleshoot and resolve issues related to ML model deployment and production performance.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field.
• Minimum of 4 years of experience in software engineering or machine learning engineering, with at least 2 years focused on MLOps.
• Proven experience in building and managing CI/CD pipelines (e.g., Jenkins, GitLab CI, GitHub Actions).
• Strong understanding of machine learning concepts and the ML lifecycle.
• Proficiency in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Hands-on experience with containerization technologies like Docker and orchestration tools like Kubernetes.
• Experience with cloud platforms (AWS, Azure, or GCP) and their ML services.
• Familiarity with infrastructure-as-code tools (e.g., Terraform, CloudFormation) is a plus.
• Excellent problem-solving and debugging skills.
• Strong communication and collaboration abilities.

Benefits

• Our client offers a competitive salary, performance-based bonuses, comprehensive health insurance, and a retirement savings plan.
• We provide opportunities for professional development, including access to online courses, workshops, and conferences.
• This role offers a hybrid work model, providing flexibility and a balance between office-based collaboration in Mossel Bay, Western Cape and remote work.
• Employees benefit from a supportive team environment, access to modern tools, and opportunities for career growth within a leading AI company.

Join us and help shape the future of AI deployment.