Senior Architect AI

Job Purpose As Senior Architect AI, you will be accountable for designing and overseeing the end-to-end AI platform architecture that powers Liberty Global's telecommunications services across our 85 million subscriber base. You'll bridge technical excellence with business value, ensuring AI solutions integrate seamlessly with our telecommunications infrastructure while meeting operational, budget, and performance requirements. This role requires deep technical expertise combined with strong communication skills to work effectively with both engineering teams and business stakeholders, translating complex technical concepts into actionable recommendations that drive business outcomes. Key Responsibilities End-to-End AI Architecture Design & Ownership Design and maintain comprehensive AI platform architecture spanning data pipelines, model development, infrastructure, deployment frameworks, and integration with both on Prem and cloud platforms and applications. Own the technical vision for AI platform evolution, ensuring architecture supports current and future business requirements, context & process across customer experience, AI innovation, and operational efficiency use cases Define architectural standards, design patterns, and technology selection criteria that enable scalable, reliable AI implementations across multiple European markets & ventures Ensure architectural decisions consider operational requirements, budget constraints, and business priorities while maintaining technical excellence Platform Integration & Technical Accountability Take accountability for AI platform integration with Liberty Global's core telecommunications infrastructure & ventures, ensuring seamless data flow and system reliability Design integration patterns and API strategies that connect AI services with existing platforms while maintaining security, performance, and data quality standards Collaborate with enterprise architects and engineering teams to ensure AI solutions align with overall technology architecture and infrastructure capabilities Establish output goals, monitoring, observability, and quality assurance practices for AI platform components, ensuring operational stability and performance targets are met Technical Guidance & Cross-Functional Collaboration Provide technical expertise and architectural guidance to engineering teams, data scientists, and product managers throughout the AI solution development lifecycle Work closely with business stakeholders to understand requirements, translate them into technical architectures, and communicate trade-offs, timelines, and resource needs clearly Review and approve technical designs, ensuring alignment with architectural standards and best practices while addressing scalability, security, and maintainability concerns Collaborate with operations teams on deployment strategies, capacity planning, and cost optimization for AI infrastructure and cloud resources Support comprehensive technical due diligence for potential acquisitions, investments, or partnerships Operational Excellence & Budget Considerations Contribute to budget planning and cost optimization discussions, providing recommendations on infrastructure investments, cloud resource allocation, and technology choices Design architectures that optimize operational efficiency through automation, resource utilization, and smart platform design while meeting performance requirements Support capacity planning and infrastructure scaling decisions based on usage patterns, performance metrics, and business growth projections Participate in vendor evaluations and technology assessments, providing technical recommendations that balance capability, cost, and operational impact Define, refine & optimise processes, frameworks & context to ensure right business outcomes Set Lifecycle management expectations and govern End of Existence models and components Define and validate business cases by establishing clear metrics, measurable outcomes, and ROI, ensuring solutions deliver tangible customer value and product impact Knowledge and Experience Essential: 8-10 years of experience in software architecture or technical lead roles with 4-5 years focused on AI/ML platforms and data-intensive systems Proven experience designing end-to-end architectures or processes for AI/ML systems in production environments supporting large user bases Understanding of IT platforms i,e cloud platforms (AWS, Azure, GCP), containerization (Kubernetes, Docker), and MLOps frameworks and practices Understanding of the use of Waterfall vs Agile practices including use of Kanban, SAFE, etc Deep understanding of telecommunications systems and integration patterns with OSS/BSS, customer platforms, and network management systems Demonstrated ability to work effectively with both technical teams and business stakeholders, translating between technical and business language Proficiency in AI/ML technologies including model training platforms, deployment frameworks, data pipelines, and monitoring tools Experience with distributed systems design, API architecture, and microservices patterns for building scalable platforms Strong knowledge of database technologies, data warehousing, streaming platforms (Kafka, Pulsar), and big data processing frameworks Excellent communication and presentation skills for explaining complex technical concepts to non-technical audiences and business stakeholders including use of deep data analysis Understanding of budget planning, cost modelling, and operational considerations for large-scale technology platforms & businesses Ability to source, process, and analyse complex datasets to generate actionable insights that inform business and product decisions Desirable: Hands-on coding and prototyping experience in AI/ML development (Python, TensorFlow, PyTorch) Experience with proof-of-concept development and technical validation of new AI technologies Background in data science or machine learning engineering with an understanding of model development workflows Practical experience with infrastructure-as-code and DevOps practices for AI platforms Knowledge of data management M&A due diligence Preferred education/qualifications: Experience in telecommunications, cable, or network infrastructure industries with understanding of subscriber analytics and operational systems Bachelor's or Master's degree in Computer Science, Engineering, Telecommunications, or related technical field Background in hands-on development with Python, Java, or similar languages (nice to have but not required for day-to-day work) Knowledge of AI ethics, responsible AI practices, and regulatory requirements relevant to European telecommunications markets or relevant knowledge gained from operations Experience with architectural frameworks and documentation practices for enterprise-scale systems