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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 and improve operational efficiency (e.g., customer service chatbots, Finance efficiencies, HR optimizations, etc). The AI Engineer will be responsible for building and optimizing AI applications, ensuring the apps are deployed effectively and integrated with existing systems.

Your impact on our goals

• Model Development: Design, develop, and deploy LLM AI models that address specific business needs, such OCR, user intent and others.

• Data Preprocessing: Work with Data Engineers to ensure that data is properly processed, cleaned, and transformed for AI applications.

• Model Optimization: Continuously monitor and optimize AI applications 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 other AI engineers, Business Analysts, and Solution Architects to ensure that AI applications 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 day to day work (e.g., Langchain, RAG, langgraph, 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 applications 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 applications and pipelines, ensuring that solutions continue to perform in changing business and technical environments.

Experience

• 5+ years of experience in software engineering roles in python, including hands-on experience with developing and deploying backend/frontend applications in production.

• Strong proficiency in Python and LLM and AI agent orchestration libraries like langchain, langgraph, OpenAI, llamaindex, fastAPI

• Worked on LLM prompt engineering, RAG applications, and AI agents frameworks and applications

• Experience with cloud-based AI platforms (e.g., Google Cloud, Azure AI).

• Familiarity with data engineering processes and data pipeline development on unstructured and structured data.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field (Master’s degree in AI, Machine Learning, or Data Science preferred).

• Knowledge of MLOps tools and techniques for version control, continuous integration, and deployment of AI applications.

• Strong understanding of distributed computing and cloud infrastructure.