Machine Learning Operations (MLOps) Engineer

About the Role

Our client is seeking a skilled Machine Learning Operations (MLOps) Engineer to join their advanced technology team in Oxford . This crucial role focuses on streamlining and automating the end-to-end machine learning lifecycle, ensuring the reliable deployment, monitoring, and maintenance of ML models in production. You will bridge the gap between data science and operations, implementing best practices in CI/CD, infrastructure management, and model governance. Join a forward-thinking organization where your expertise will be vital in scaling their AI capabilities and ensuring the robust performance of their machine learning systems.

Key Responsibilities

• Design, build, and maintain scalable and reliable MLOps infrastructure and pipelines.
• Automate the deployment, monitoring, and updating of machine learning models in production environments.
• Implement continuous integration, continuous delivery (CI/CD) practices for machine learning workflows.
• Manage and optimize cloud infrastructure (e.g., AWS, Azure, GCP) supporting ML model deployment and operations.
• Develop strategies for model versioning, experiment tracking, and performance monitoring.
• Collaborate with data scientists and software engineers to ensure smooth model lifecycle management.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field.
• Proven experience in MLOps, DevOps, or a related role focused on automating ML workflows.
• Strong understanding of machine learning concepts and the ML lifecycle.
• Proficiency in scripting languages (e.g., Python) and experience with containerization technologies (Docker, Kubernetes).
• Experience with cloud platforms and their ML services, as well as infrastructure-as-code tools (e.g., Terraform).
• Familiarity with CI/CD tools and version control systems (e.g., Git).

Benefits

• Competitive salary and comprehensive benefits package.
• Opportunities for professional development and training in cutting-edge MLOps technologies.
• A collaborative and innovative work environment in Oxford .
• Excellent work-life balance and opportunities for career advancement.
• Be a key player in enabling the scalable deployment of AI.