Senior Architect AI

Role purpose

Architecture and integration

• Design architecture covering data pipelines, model development, infrastructure, deployment frameworks, and on-premise and cloud platforms.
• Own the technical vision and define standards, design patterns, and technology selection criteria.
• Integrate AI platforms with telecommunications infrastructure and existing applications.
• Design API and integration patterns that maintain security, performance, reliability, and data quality.
• Establish monitoring, observability, output goals, and quality assurance practices.

Technical leadership

• Guide engineering teams, data scientists, and product managers through AI solution development.
• Translate business requirements into technical architectures and communicate trade-offs, timelines, and resource needs.
• Review and approve designs for scalability, security, maintainability, and architectural alignment.
• Support deployment strategies, capacity planning, and cloud cost optimization.
• Support technical due diligence for acquisitions, investments, and partnerships.

Operations and value

• Contribute to infrastructure investment, cloud allocation, vendor evaluation, and technology selection decisions.
• Define and optimize processes and frameworks to achieve business outcomes.
• Set lifecycle management expectations and govern end-of-existence models and components.
• Define business cases, metrics, measurable outcomes, ROI, customer value, and product impact.

Essential experience

• 8–10 years in software architecture or technical lead roles, including 4–5 years focused on AI/ML platforms and data-intensive systems.
• Production experience designing end-to-end AI/ML architectures for large user bases.
• Understanding of cloud platforms, Kubernetes, Docker, MLOps, and Agile and Waterfall practices.
• Deep telecommunications systems knowledge, including OSS/BSS, customer platforms, and network management systems.
• Proficiency in AI/ML platforms, deployment frameworks, data pipelines, and monitoring tools.
• Experience with distributed systems, API architecture, microservices, databases, streaming, and big data.
• Strong communication, data analysis, budget planning, and cost modelling capabilities.

Desirable experience

• Hands-on coding or prototyping with Python, TensorFlow, or PyTorch.
• Experience with proof-of-concept development, data science, machine learning engineering, infrastructure as code, or DevOps.
• Experience in telecommunications, cable, or network infrastructure industries.
• A degree in Computer Science, Engineering, Telecommunications, or a related technical field is preferred.
• Knowledge of AI ethics, responsible AI, regulatory requirements, or enterprise architectural frameworks is preferred.