Machine Learning Operations (MLOps) Engineer

Placements24; Windsor Park (TN); Hybrid; USD 37,000 - 55,000; Full time

Benefits offered by this job

Competitive salary and bonus

Hybrid work model

Health and retirement benefits

Learning and development

Cutting-edge MLOps tools

Collaborative team environment

Generous paid time off

Employee assistance programs

Job summary

Placements24 in East London, ZA is seeking an experienced MLOps Engineer to design and operate scalable ML pipelines, deploy models, and monitor production systems. The role blends development and operations to keep AI workflows reliable and efficient.

You will collaborate with data scientists and software engineers, manage cloud resources, and implement best practices for versioning, security, and governance across ML lifecycles in a hybrid work setup.

Qualifications

• Bachelor's degree in Computer Science, Engineering or related field.
• 3+ years of experience in software engineering, DevOps, or MLOps.
• Proficiency in Python scripting.
• Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Familiarity with CI/CD tools (Jenkins, GitLab CI, GitHub Actions).
• Understanding of ML concepts and lifecycles.
• Experience with ML orchestration tools (Kubeflow, MLflow, Airflow).
• Strong problem-solving and attention to detail.

Responsibilities

• Design, implement, and manage MLOps pipelines for model training, deployment, and monitoring.
• Develop and maintain CI/CD pipelines for ML models.
• Automate deployment, scaling, and management of ML models in production.
• Implement robust monitoring for model performance, data drift, and system health.
• Collaborate with data scientists and software engineers for seamless ML integration.
• Manage and optimize cloud infrastructure for ML workloads (AWS, Azure, GCP).
• Implement tools for version control of data, code, and models.
• Ensure security and governance standards for ML systems.

Skills

Problem solving

Attention to detail

Team collaboration

Education

Bachelor's degree in Computer Science, Engineering, or a related field

Tools

Python

Docker

Kubernetes

AWS

Azure

GCP

Jenkins

GitLab CI

GitHub Actions

Kubeflow

MLflow

Airflow

Job description

About the Role

Our client is a rapidly growing AI company seeking a skilled Machine Learning Operations (MLOps) Engineer to join our team in East London, Eastern Cape, ZA. This role is critical for streamlining and scaling our machine learning lifecycle, from model development to deployment and monitoring. We offer a hybrid work arrangement, providing a balance between remote flexibility and in-office collaboration. You will play a key role in building and maintaining robust MLOps infrastructure, enabling our data science and engineering teams to deliver AI solutions efficiently and reliably. This is a fantastic opportunity to shape the operational excellence of AI development.

Key Responsibilities

• Design, implement, and manage MLOps pipelines for model training, deployment, and monitoring.
• Develop and maintain CI/CD pipelines for machine learning models.
• Automate the deployment, scaling, and management of ML models in production environments.
• Implement robust monitoring solutions for model performance, data drift, and system health.
• Collaborate with data scientists and software engineers to ensure seamless integration and operation of ML models.
• Manage and optimize cloud infrastructure for ML workloads (e.g., AWS, Azure, GCP).
• Implement tools and best practices for version control of data, code, and models.
• Ensure compliance with security and governance standards for ML systems.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 3+ years of experience in software engineering, DevOps, or MLOps, with a focus on ML systems.
• Proficiency in scripting languages such as Python.
• Experience with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
• Familiarity with CI/CD tools (e.g., Jenkins, GitLab CI, GitHub Actions).
• Understanding of machine learning concepts and lifecycles.
• Experience with ML orchestration tools (e.g., Kubeflow, MLflow, Airflow).
• Strong problem-solving skills and attention to detail.

Benefits

• Competitive salary and bonus opportunities.
• Hybrid work model for enhanced work-life balance.
• Comprehensive health and retirement benefits.
• Opportunities for continuous learning and professional development.
• Access to cutting-edge MLOps tools and technologies.
• Collaborative and dynamic team environment.
• Generous paid time off.
• Employee assistance programs.