Machine Learning Operations (MLOps) Engineer

About the Role

Our client, a dynamic force in AI & Emerging Technologies , is seeking a skilled Machine Learning Operations (MLOps) Engineer to join their dedicated team in Liverpool . This role is crucial for bridging the gap between machine learning model development and reliable production deployment. You will be responsible for building and maintaining the infrastructure, pipelines, and tools necessary to efficiently manage the lifecycle of ML models. Working closely with data scientists and software engineers in an on-site capacity, you will ensure the scalability, reliability, and performance of our client's AI systems in real-world applications.

Key Responsibilities

• Design, build, and automate robust ML pipelines for continuous integration, delivery, and deployment (CI/CD) of models.
• Implement and manage infrastructure for training, deploying, and monitoring machine learning models at scale.
• Develop and maintain tools and frameworks for model versioning, experiment tracking, and performance monitoring.
• Ensure the reliability, scalability, and efficiency of ML systems in production environments.
• Collaborate with data scientists and engineers to optimize model performance and troubleshoot deployment issues.
• Implement security best practices for ML infrastructure and data handling.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related field.
• 3+ years of experience in software engineering, DevOps, or MLOps.
• Strong experience with cloud platforms (AWS, Azure, or GCP) and containerization technologies (Docker, Kubernetes).
• Proficiency in scripting languages like Python and experience with ML frameworks.
• Familiarity with CI/CD tools and practices.
• Excellent problem-solving skills and the ability to work effectively in a collaborative, in-office setting in Liverpool .

Benefits

• Competitive salary and performance-related bonus structure.
• Comprehensive health, dental, and vision insurance coverage.
• Opportunities for professional development and certifications in MLOps.
• Access to modern office facilities and collaborative workspaces.
• Engaging work environment contributing to the advancement of AI technologies.