Machine Learning Operations (MLOps) Engineer

About the Role

Our client is seeking an experienced Machine Learning Operations (MLOps) Engineer to join their innovative AI team in Oxford . This role is central to ensuring the seamless deployment, monitoring, and maintenance of machine learning models in production environments. You will bridge the gap between data science and operations, building and automating robust pipelines that support the full ML lifecycle. This is an excellent opportunity to work with cutting-edge technologies and contribute to the operational excellence of our AI solutions in a hybrid work setting.

Key Responsibilities

• Design, build, and maintain scalable and reliable MLOps pipelines for continuous integration, delivery, and deployment of machine learning models.
• Implement and manage infrastructure for model training, serving, and monitoring using cloud platforms and containerization technologies (e.g., Docker, Kubernetes).
• Develop automation scripts and tools to streamline the ML workflow, from data ingestion to model retraining.
• Collaborate with data scientists and software engineers to troubleshoot issues, optimize model performance, and ensure system stability.
• Implement monitoring solutions to track model performance, drift, and system health in production.
• Ensure security, scalability, and cost-efficiency of the ML infrastructure.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related quantitative field.
• Proven experience as an MLOps Engineer, DevOps Engineer, or Software Engineer with a focus on ML infrastructure.
• Strong proficiency in scripting languages (e.g., Python, Bash) and experience with cloud platforms (AWS, Azure, GCP).
• Hands-on experience with containerization (Docker, Kubernetes) and CI/CD tools.
• Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and model deployment strategies.
• Understanding of data pipelines, version control (e.g., Git), and infrastructure-as-code tools.

Benefits

• Competitive salary and performance-based incentives.
• Hybrid working model offering a blend of office and remote work.
• Comprehensive health, dental, and vision insurance coverage.
• Generous pension scheme and life assurance.
• Opportunities for professional development and upskilling in AI and MLOps.