Machine Learning Operations (MLOps) Engineer

Placements24; London Borough of Newham; Hybrid; GBP 41,000 - 63,000; Full time

Benefits offered by this job

Hybrid work model

Competitive salary + bonus

Health & retirement benefits

Professional development

Generous PTO

Employee assistance program

Job summary

Placements24 is assisting a rapidly growing AI company in East London, ZA, seeking an experienced MLOps Engineer to design and manage end-to-end ML pipelines, deploy models, and run robust monitoring.

You will collaborate with data scientists and software engineers, optimize cloud infrastructure across AWS/Azure/GCP, implement CI/CD, and help secure governance and data versioning in production environments. Hybrid work options available.

Qualifications

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

Responsibilities

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

Skills

Python

Cloud platforms

Docker & Kubernetes

CI/CD tooling

ML concepts & pipelines

Education

Bachelor's degree in CS/Engineering

Tools

Kubeflow

MLflow

Apache 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.