AI Engineer

Job description: Our business landscape We’re an award-winning global communications company operating in nine countries across the Middle East, North Africa, and Southeast Asia. Our strategy is to become the region’s leading digital infrastructure provider. Ooredoo Group’s strategic vision is guided by five key pillars: • Value-Focused Portfolio: Boosting asset returns by focusing on telco operations, towers, data centres, the sea cable business and fintech. • Strengthen the Core: Optimally using deployed capital and maintain an appropriate cost structure. • Evolve the Core: Monetising opportunities to generate new revenue streams via programmes focusing on analytics, digitalisation of operations, and partnerships with digital service providers. • People: Building an engaged and empowered workforce through integrated learning programs and coaching and mentoring. • Excellence in Customer Experience: Creating superior customer experiences. From day one, every employee who joins our team becomes an integral part of our success journey. We offer you the chance to enhance your skills, advance your career, and maintain a healthy work- life balance. Empowering you to catapult your personal and professional growth. If you’re looking to challenge your growth potential, Ooredoo is the employer for you. Job overview 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. Your impact on our goals • 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. • Data Preprocessing: Work with Data Engineers to ensure that data is properly processed, cleaned, and transformed for AI model training and testing. • Model Optimization: Continuously monitor and optimize AI models in production to ensure they meet performance targets and deliver expected business value. • AI Pipeline Management: Design and maintain scalable and efficient AI pipelines, ensuring that models are deployed and maintained across all OpCos with minimal disruptions. • 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. • 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). • 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. • 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. • Troubleshooting: Diagnose and resolve issues in AI models and pipelines, ensuring that solutions continue to perform in changing business and technical environments. Team dynamics Internal • Group employees on overall AI strategy • OpCo analysts and leaders on adapting the AI models External • Evaluate different vendors based on their AI solutions and their specific advantage over in-house solution Your profile Experience • 5+ years of experience in AI or machine learning engineering roles, including hands-on experience with developing and deploying machine learning models. • Has telecom experience with some expertise in network planning, optimization, or operations • Strong proficiency in Python and machine learning libraries such as TensorFlow, PyTorch, and scikit-learn. • Experience with cloud-based AI platforms (e.g., AWS, Google Cloud, Azure AI). • Familiarity with data engineering processes and data pipeline development. Qualifications • Bachelor’s degree in Computer Science, Engineering, or related field (Master’s degree in AI, Machine Learning, or Data Science preferred). • Experience in the telecommunications or mobile technology industry. • Knowledge of MLOps tools and techniques for version control, continuous integration, and deployment of AI models. • Strong understanding of distributed computing and cloud infrastructure. • Experience with reinforcement learning or advanced machine learning techniques. Must-have skills Technical • Machine Learning Expertise: Strong understanding of machine learning algorithms, deep learning architectures, and model optimization techniques. • AI Engineering: Proven ability to build, test, deploy, and maintain AI models at scale, ensuring they meet both technical and business requirements. • Collaboration: Excellent collaboration skills to work closely with Data Scientists, Business Analysts, and the wider technology team. • Problem-solving: Demonstrated ability to diagnose and resolve issues in machine learning models and production pipelines. • Data Processing: Experience with data wrangling, preprocessing, and feature engineering techniques. Behavioural • Networking & Influencing Collaboratively - Intermediate • Delivering Results & Fostering Ownership - Intermediate • Building Customer Value - Intermediate Show more

Design, develop, and deploy machine learning and AI models that address specific business needs, such as customer retention, personalized marketing, and operational automation. Data Preprocessing: Work with Data Engineers to ensure that data is properly processed, cleaned, and transformed for AI model training and testing. Model Optimization: Continuously monitor and optimize AI models in production to ensure they meet performance targets and deliver expected business value. AI Pipeline Management: Design and maintain scalable and efficient AI pipelines, ensuring that models are deployed and maintained across all OpCos with minimal disruptions. 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. 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). 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. 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. Troubleshooting: Diagnose and resolve issues in AI models and pipelines, ensuring that solutions continue to perform in changing business and technical environments.