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 in n ovation , 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