GenAI Solutions Architect and Technical Lead

4 settimane fa

Milano, Lombardia, Italia Vodafone A tempo pieno

Role Purpose:

As a Senior AI Architect and Technical Lead for GenAI Solutions at Vodafone, you will be responsible for architecting, leading, and owning the full lifecycle of generative AI initiatives across the organization. This role requires expertise in advanced AI technologies, including knowledge graphs, vector databases, and retrieval-augmented generation (RAG) systems. You will take ownership of shaping architectures, technical design, deployment, and continuous optimization of AI-driven systems that transform business processes, ensuring scalability, security, and compliance.

Key Accountabilities and Decision Ownership:

• Architect AI Solutions: Design and architect GenAI solutions with a focus on leveraging knowledge graphs, vector databases, and RAG frameworks to enable intelligent data retrieval and context-aware AI applications.

• Technical Ownership: Act as the technical owner for all GenAI 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 GenAI 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:

• Proven AI Leadership: Minimum 7 years of experience in AI/ML, with a focus on designing and leading the implementation of generative AI solutions, including at least 3 years in a technical leadership or solution architect role.

• Knowledge Graph Expertise: Strong understanding and hands-on experience with knowledge graph technologies (e.g., Neo4j, GraphDB, 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 (TensorFlow, PyTorch), 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 languages commonly used for AI and machine learning development.

• 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:

• Telecom Industry Experience: Familiarity with the telecommunications industry, particularly with AI-driven customer engagement platforms, personalization engines, and operational optimization tools.

• Agile & DevOps Methodologies: Experience with Agile development and DevOps 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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