The AI Engineer will design, develop, and deploy AI models to address business challenges and opportunities across the Group. This role involves working closely with the Solution Architect, Data Scientists, and Business Analysts to implement AI solutions that generate revenue (e.g., personalized marketing, customer value management) and improve operational efficiency (e.g., customer service chatbots, network fault diagnostics). The AI Engineer will be responsible for building and optimizing AI pipelines, ensuring models are deployed effectively and integrated with existing systems.

1. Model Development: Design, develop, and deploy machine learning and AI models that address specific business needs, such as customer retention, personalized marketing, and operational automation.
2. Data Preprocessing: Work with Data Engineers to ensure that data is properly processed, cleaned, and transformed for AI model training and testing.
3. Model Optimization: Continuously monitor and optimize AI models in production to ensure they meet performance targets and deliver expected business value.
4. AI Pipeline Management: Design and maintain scalable and efficient AI pipelines, ensuring that models are deployed and maintained across all OpCos with minimal disruptions.
5. Collaboration: Work closely with Data Scientists, Business Analysts, and Solution Architects to ensure that AI models align with business objectives and integrate smoothly into existing workflows.
6. AI Tooling and Infrastructure: Identify and implement AI tools and frameworks that improve efficiency and scalability of AI model development and deployment (e.g., TensorFlow, PyTorch, scikit-learn, cloud-based AI services).
7. Research and Innovation: Stay updated on the latest developments in AI, machine learning, and related technologies. Bring new ideas and innovations to the team that can be applied to the Group’s AI initiatives.
8. Deployment & MLOps: Collaborate with ML Ops Engineers to ensure that AI models are deployed reliably and can be scaled across multiple regions (OpCos). Develop and follow best practices for AI model versioning, monitoring, and updates.
9. Troubleshooting: Diagnose and resolve issues in AI models and pipelines, ensuring that solutions continue to perform in changing business and technical environments.