GenAI Solutions Architect

1 settimana fa

Milano, Lombardia, Italia Vodafone A tempo pieno

Role Purpose:

As a GenAI Solutions Architect at Vodafone, you will lead the design, development, and implementation of Generative AI solutions within our technology ecosystem. You will collaborate with cross-functional teams to architect AI-driven solutions that enhance business processes, improve customer experiences, and drive operational efficiencies.

Key Accountabilities and Decision Ownership:

Solution Design & Architecture: Design and architect scalable, secure, and high-performance GenAI solutions to meet business needs across various departments.

Innovation & Strategy: Develop AI strategies that align with Vodafone's long-term goals, focusing on cutting-edge generative AI technologies.

Collaboration & Stakeholder Management: Work closely with business leaders, data scientists, AI engineers, and external partners to gather requirements, present solutions, and ensure successful solution delivery.

Technology Stack & Integration: Architect solutions that integrate with Vodafone's existing tech stack, including cloud platforms, APIs, and data analytics tools.

Model Optimization & Deployment: Collaborate with AI and ML teams to ensure the proper training, fine-tuning, and deployment of generative models that are optimized for scale and performance.

Security & Compliance: Ensure that all GenAI solutions adhere to the highest security, privacy, and regulatory standards, including GDPR, and Vodafone's internal policies.

Continuous Improvement: Stay updated on the latest trends in AI and machine learning technologies and apply them to enhance existing solutions.

Mentorship & Leadership: Provide technical leadership to junior architects and engineers, fostering a culture of innovation and excellence within the AI team.

Required Competencies, Knowledge, and Experience:

Proven Experience in AI/ML: At least 5 years of experience in AI/ML, with a strong focus on architecting generative AI solutions.

Technical Expertise: Deep understanding of generative models, NLP, transformers, and large language models, including GPT and BERT.

Cloud Expertise: Hands-on experience with cloud platforms, including AWS, Azure, or Google Cloud, with a focus on AI/ML services and infrastructure.

Solution Architecture: Experience in designing large-scale, distributed AI systems with an understanding of microservices architecture, APIs, and modern data platforms.

Programming Languages: Strong skills in Python, Java, or other relevant languages used in AI development.

Data Integration: Expertise in working with structured and unstructured data, data pipelines, and real-time data processing frameworks.

Security & Compliance: Familiarity with GDPR, AI ethics, and data privacy practices, especially as they pertain to AI technologies.

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