Machine Learning Operations (MLOps) Engineer

About the Role

Our client is seeking an experienced Machine Learning Operations (MLOps) Engineer to join their advanced technology team in Wolverhampton . 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 robust, scalable, and automated pipelines for ML model deployment, monitoring, and management. If you have a strong background in both software engineering and machine learning infrastructure, this is a prime opportunity to shape operational excellence in AI.

Key Responsibilities

• Design, build, and maintain scalable and automated MLOps pipelines for model deployment, testing, and monitoring.
• Implement strategies for continuous integration, continuous delivery (CI/CD), and continuous training (CT) for machine learning models.
• Develop and manage infrastructure for training and serving ML models, often leveraging cloud platforms.
• Monitor model performance in production, identify drift or degradation, and implement retraining strategies.
• Collaborate with data scientists and software engineers to ensure smooth model lifecycle management.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related technical field.
• Proven experience (5+ years) in software engineering or DevOps, with a specific focus on MLOps.
• Strong understanding of ML principles and experience with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Proficiency with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
• Experience with CI/CD tools (e.g., Jenkins, GitLab CI) and scripting languages (e.g., Python, Bash).

Benefits

• Competitive salary and performance-based incentives.
• Comprehensive health, dental, and vision insurance.
• Generous allowance for professional development and training.
• A dynamic and challenging work environment focused on cutting-edge AI operations.
• Opportunities to work on diverse and impactful machine learning projects.