The ML Ops Engineer will be responsible for managing the deployment, monitoring, and  maintenance of machine learning models in production environments. Reporting to the AI  Solutions Manager, this role focuses on ensuring the scalability, reliability, and efficiency of AI  solutions across the organization. The ideal candidate will have a strong background in machine  learning operations, cloud infrastructure, and DevOps practices.   Key Responsibilities:  Model Deployment: Design and implement scalable deployment pipelines for machine learning models, ensuring seamless integration with existing systems and applications.  Monitoring and Maintenance: Develop monitoring and alerting solutions to track model performance and operational metrics in real-time. Implement strategies for model retraining and updates based on performance feedback.  Infrastructure Management: Manage cloud-based infrastructure and resources to support machine learning workloads. Optimize resource utilization and ensure cost-effectiveness.  Collaboration: Work closely with data scientists, AI developers, and IT teams to facilitate the transition of models from development to production. Ensure alignment with business and technical requirements.  Security and Compliance: Implement best practices for data security, privacy, and compliance in all ML Ops processes. Ensure adherence to industry standards and regulations.  Automation: Develop automated workflows for continuous integration and continuous deployment (CI/CD) of machine learning models. Streamline processes to improve efficiency and reduce manual intervention.  Documentation: Maintain comprehensive documentation of ML Ops processes, system architectures, and deployment configurations. Provide training and support to team members on ML Ops best practices.    Qualifications:  Bachelor’s degree in Computer Science, Engineering, or a related field or equivalent experience.  2+ years of experience in machine learning operations, DevOps, or related fields.  Proficiency in programming languages such as Python and experience with ML frameworks like TensorFlow, PyTorch, or similar.  Strong understanding of cloud platforms (Azure, AWS, Google Cloud) and containerization technologies (Docker, Kubernetes).  Experience with CI/CD tools and practices.  Excellent problem-solving skills and the ability to work independently and collaboratively.  Strong communication skills to effectively convey technical concepts to non-technical stakeholders.  Familiarity with data security and compliance requirements in machine learning environments.   Introduction Requirements Information Organisation/Department Job description