Role purpose:

Key accountabilities and decision ownership:

• Technical Ownership: Act as the technical owner for all Gen AI initiatives, ensuring the architectural integrity, system performance, and security of AI solutions throughout their lifecycle.
• AI Solution Delivery: Lead the end-to-end delivery of AI solutions, ensuring they meet performance, scalability, and security requirements, while continuously improving and optimizing the models.
• Strategic Roadmap: Define the long-term technology roadmap for Gen AI and related AI technologies, ensuring alignment with Vodafone’s business goals and digital transformation strategies.
• Collaboration: Collaborate with AI engineers, data scientists, and business stakeholders to deliver AI solutions that effectively meet complex business needs, ensuring seamless integration with Vodafone’s existing systems.
• Security & Compliance: Ensure all AI solutions, particularly those leveraging sensitive data, comply with security, privacy, and regulatory standards (including GDPR) and Vodafone’s internal governance policies.
• Leadership & Mentorship: Provide leadership and guidance to AI teams, including architects, engineers, and data scientists, to foster innovation, technical growth, and excellence.
• Stakeholder Communication: Act as the primary technical point of contact for senior leadership, clearly communicating complex technical solutions, project status, and potential risks.

Required competencies, knowledge, and experience:

• Knowledge Graph Expertise: Strong understanding and hands-on experience with knowledge graph technologies (e.g., Neo4j, Graph DB, or RDF-based systems) and their role in enriching AI models with contextual, relational data.
• Vector Database Knowledge: Deep experience with vector databases (e.g., Pinecone, Weaviate, FAISS) for managing and querying high-dimensional data in AI applications, particularly for semantic search and information retrieval tasks.
• RAG Systems: Expertise in building Retrieval-Augmented Generation (RAG) systems, which enhance generative models by integrating real-time data retrieval from external knowledge bases or databases, improving the accuracy and relevance of outputs.
• Generative AI & NLP Expertise: Proven experience with large language models (LLMs) like GPT, BERT, and other transformer-based architectures. Extensive knowledge in natural language processing (NLP), deep learning frameworks (Tensor Flow, Py Torch), and AI model fine-tuning.
• Solution Architecture: Expertise in architecting large-scale AI solutions that are scalable, distributed, and secure, with experience in microservices architecture, API development, and cloud integration.
• Cloud & AI Services: Extensive experience with cloud platforms (AWS, Azure, Google Cloud) for deploying AI/ML models and managing AI infrastructure.
• Programming Languages: Proficiency in Python, Java, or other

• Security & Compliance: Strong understanding of security, privacy, and regulatory standards (e.g., GDPR) as they pertain to AI/ML solutions, especially in sensitive data environments.
• Collaboration & Communication: Excellent communication skills, with a demonstrated ability to translate technical concepts into business language for non-technical stakeholders.

Nice to have technical and professional qualifications:

• Agile & Dev Ops Methodologies: Experience with Agile development and Dev Ops practices for rapid AI development and deployment.
• AI Certifications: AI-related certifications (e.g., AWS/Azure Certified Machine Learning, Google Professional Cloud Architect,).

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