Manager AI Products

Role :The Manager AI Products is the primary builder within the AI Hub, responsible for turning architectural designs and product specifications into working, production-deployed AI systems.This role spans the full technical stack of modern AI engineering: training and fine-tuning ML models, building LLM powered applications, developing agentic workflows, and integrating AI solutions with Ooredoo's enterprise platform ecosystem. The AI Engineer works in close partnership with the AI Solutions Architect consuming architecture blueprints and raising implementation feedback and is instrumental in maintaining the reliability, performance, and continuous improvement of deployed AI systems.About the Business Unit:AI & Data Governance team is responsible for developing and implementing data governance frameworks and AI strategies to ensure data integrity, security, compliance, and to drive innovation and operational excellence within Ooredoo. Additionally, the team manages AI projects and proof of concepts, tracks data initiatives, and coordinates with cross functional teams to ensure timely delivery and value realization.Minimum Experience, Essential Knowledge & Skills:10 years' experience in a similar role.Strong proficiency in Python and its AI/ML ecosystem: PyTorch or TensorFlow, Hugging Face Transformers, Lang Chain, and related libraries.Practical experience building LLM applications prompt engineering, RAG pipelines, embedding models, vector databases (e.g., Pinecone, Weaviate,Azure AI Search).Experience with major cloud AI services: Azure OpenAI Service, Azure AI Studio, AWS Bedrock, or Google Vertex AI.Solid software engineering fundamentals: REST API design and consumption, containerisation (Docker/Kubernetes), CI/CD pipelines, and Git-based workflows.Experience integrating AI systems with enterprise software platforms via APIs or event-driven architecture.Experience in designing, developing, and orchestrating agent-to-agent (A2A) solutions, enabling autonomous collaboration, decision-making, and task execution across multiple AI agents platforms.Experience with agentic AI frameworks: LangGraph, AutoGen, CrewAI, or Microsoft Semantic Kernel multi-agent orchestration.Familiarity with MLOps tooling: MLflow, Azure MLPipelines, Kubeflow, or similar model lifecyclemanagement platforms.Knowledge of BSS/OSS systems or CRM integration patterns in a telecommunications context.Experience with speech-to-text, text-to-speech, or multi-modal AI for voice-enabled self-service applications.Minimum Qualifications:Bachelor's Degree in Computer Science or Engineering or Similar