Manager AI Products

Role Summary

The Manager AI Products serves as the core engineer within the AI Hub, tasked with transforming architectural plans and product specs into operational AI systems deployed in production environments. This position covers the entire spectrum of AI engineering from training and refining machine learning models to creating large language model (LLM)-powered applications, building agentic workflows, and embedding AI solutions within Ooredoo's enterprise platforms. The role requires close collaboration with the AI Solutions Architect to interpret architecture blueprints and provide implementation insights, maintaining system reliability, optimal performance, and continual enhancements of deployed AI assets.

Business Unit Overview

The AI & Data Governance team drives the establishment and execution of data governance frameworks and AI strategies aimed at preserving data integrity, enhancing security, ensuring compliance, and fostering innovation and operational excellence at Ooredoo. The team also oversees AI initiatives, proof of concepts, monitors data projects, and collaborates across functions to ensure on-time delivery and realization of value.

Experience, Skills & Knowledge

• At least 10 years of professional experience in a comparable position.
• Expertise with Python programming and AI/ML libraries including PyTorch or TensorFlow, Hugging Face Transformers, Lang Chain, and others in the ecosystem.
• Hands-on experience designing and deploying LLM applications, prompt engineering, retrieval augmented generation (RAG) pipelines, embedding models, and working with vector databases like Pinecone, Weaviate, or Azure AI Search.
• Proficiency with leading cloud AI platforms such as Azure OpenAI Service, Azure AI Studio, AWS Bedrock, or Google Vertex AI.
• Strong foundation in software engineering practices: REST API development and consumption, containerization via Docker and Kubernetes, implementation of CI/CD pipelines, and proficiency with Git workflow.
• Capability to integrate AI solutions with enterprise software through APIs or event-driven architectures.
• Experience designing, developing, and managing agent-to-agent (A2A) AI solutions that enable autonomous collaboration, decision-making, and task execution across multiple AI-agent systems.
• Familiarity with agentic AI orchestration frameworks such as LangGraph, AutoGen, CrewAI, or Microsoft Semantic Kernel.
• Knowledge of MLOps platforms like MLflow, Azure ML Pipelines, Kubeflow, or similar for model lifecycle management.
• Understanding of telecommunications software systems like BSS/OSS or CRM integration practices.
• Experience implementing speech-to-text, text-to-speech, or multimodal AI technologies for voice-enabled self-service applications.

Minimum Educational Qualification

Bachelor's Degree in Computer Science, Engineering, or a related field.