1. MLOps Framework Development: Design, develop, and maintain a comprehensive MLOps framework, defining best practices for model deployment, monitoring, and lifecycle management. Ensure these standards are documented and easily accessible for OpCo teams.
2. Process Standardization: Establish and promote standardized processes, methodologies, and templates for MLOps activities across OpCos, ensuring consistency in AI model deployment and operations.
3. Tooling and Automation: Identify, implement, and manage MLOps tools (e.g., CI/CD pipelines, model monitoring, versioning tools) that streamline model deployment and management processes. Provide guidance on tool adoption at the OpCo level.
4. Technical Support and Troubleshooting: Serve as the central point of expertise for complex MLOps issues, providing support and guidance to local OpCo ML Ops teams. Troubleshoot and resolve technical challenges related to AI model deployment and operationalization.
5. Best Practice Dissemination: Conduct training sessions, workshops, and regular knowledge-sharing activities to disseminate best practices and ensure local teams are equipped to follow established MLOps frameworks.
6. Cross-functional Collaboration: Collaborate closely with Central AI Engineers, Data Engineers, and Solution Architects to align MLOps practices with AI model development and data engineering workflows.
7. Performance Monitoring and Optimization: Develop and implement centralized monitoring strategies for tracking model performance, ensuring that deployed models across all OpCos meet performance, scalability, and compliance requirements.
8. Governance and Compliance Oversight: Ensure that all AI models deployed across OpCos comply with the organization’s governance, ethical AI, and data privacy standards. Implement mechanisms for continuous compliance monitoring.
9. Innovation and Continuous Improvement: Stay updated on emerging MLOps trends and technologies, continuously refining and updating the Group’s MLOps framework to incorporate new tools and practices that enhance operational efficiency and model performance.

Experience

1. 5+ years of experience in MLOps, DevOps, or a similar role with a focus on machine learning or data science operations.

1. Proven experience in designing and implementing MLOps frameworks at scale, preferably in multi-location or multi-country environments.
2. Strong understanding of machine learning model lifecycle management, including deployment, versioning, and monitoring.
3. Expertise in CI/CD pipelines, containerization, and orchestration tools (e.g., Jenkins, GitLab, Docker, Kubernetes).
4. Familiarity with cloud-based AI platforms (e.g., AWS SageMaker, Azure ML, Google Cloud AI) and MLOps tools (e.g., MLflow, Kubeflow).

Qualifications

1. Bachelor’s degree in Computer Science, Engineering, or a related field (Master’s degree in AI, Machine Learning, or Data Science preferred).
2. Experience working in telecommunications or large-scale technology environments.
3. Knowledge of data privacy regulations and compliance frameworks, especially in the Middle East and North Africa (MENA) region.
4. Familiarity with distributed computing and big data technologies (e.g., Apache Spark, Hadoop).

Must Have Skills

Technical

1. MLOps Framework Design: Strong understanding of MLOps principles, best practices, and tools. Ability to develop and document standardized MLOps processes for diverse teams.
2. Collaboration and Training: Experience in working closely with cross-functional teams and in conducting training to ensure consistent application of MLOps practices.
3. Technical Problem-solving: Proven ability to diagnose and resolve complex issues related to AI model deployment and operations.
4. Automation Expertise: Hands-on experience with CI/CD pipelines, automation frameworks, and cloud-based AI services to streamline model deployment.
5. Governance and Compliance: Understanding of data privacy, governance, and ethical AI standards. Ability to implement and enforce compliance measures for AI models in production.