Machine Learning Operations (MLOps) Engineer

About the Role

Our client is seeking a highly skilled Machine Learning Operations (MLOps) Engineer to join their innovative team, initially based in Reading , with flexible remote options. This role is critical for bridging the gap between machine learning model development and production deployment, ensuring that ML models are deployed efficiently, reliably, and at scale. You will be responsible for building and managing the infrastructure, pipelines, and processes that enable seamless integration of ML models into operational systems. This is an exciting opportunity for an individual passionate about automation, CI/CD, and scaling machine learning applications within a dynamic tech environment.

Key Responsibilities

• Design, implement, and maintain CI/CD pipelines for machine learning models.
• Automate the deployment, monitoring, and retraining of ML models in production environments.
• Develop and manage infrastructure for ML experimentation and production, leveraging cloud services.
• Implement robust monitoring and alerting systems for ML model performance and health.
• Collaborate closely with data scientists and software engineers to ensure smooth model lifecycle management.
• Establish best practices for MLOps, version control, and reproducibility in ML workflows.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical discipline.
• Proven experience (4+ years) in MLOps, DevOps, or Software Engineering with a focus on ML systems.
• Strong proficiency in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Experience with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
• Familiarity with CI/CD tools (e.g., Jenkins, GitLab CI, GitHub Actions) and infrastructure as code (IaC) tools.
• Excellent problem-solving and debugging skills.

Benefits

• Competitive salary and performance-based bonuses.
• Comprehensive health, dental, and vision insurance.
• Opportunities for professional development and certifications in MLOps.
• Flexible remote working options and a supportive work-life balance.
• A dynamic and collaborative environment working on cutting-edge AI projects.