Senior AI Architect

Grow with us

The AI Architect is responsible for defining and steering the AI strategy for telecommunications network management, working closely with the AI Architect to establish the target AI implementation architecture and a phased roadmap toward achieving full autonomy in network operations, aligned with TM Forum Autonomous Network Level 4 and Level 5.

A core aspect of the role is active engagement with systems analysts and development teams across the organization, ensuring that AI-related design, implementation, and operational practices are consistent, scalable, and aligned with the agreed strategic and architectural direction.

This is a strategic role, shaping how AI is applied coherently across products, platforms, and domains rather than delivering isolated solutions.

What you will Do

• Define and support AI strategies for telecom network management and automation.
• Identify business and operational opportunities where AI can improve network performance, assurance and efficiency.
• Lead or support AI proof-of-concepts, including defining the problem, data requirements, target users, success measures and technical approach.
• Translate successful proofs of concept into scalable product, platform or operational capabilities.
• Work with systems analysts, architects and development teams to align AI solutions with broader OSS, orchestration, assurance and cloud-native architectures.
• Support the design of AI-enabled control loops, decision-making processes, confidence scoring, explainability, guardrails and rollback mechanisms.
• Contribute to roadmaps for intelligent, intent-driven and increasingly autonomous network operations.
• Promote reusable AI patterns, services, data abstractions and design practices across teams.
• Communicate AI opportunities, risks and implementation choices to technical, product and senior business stakeholders.
• Help establish appropriate governance, observability, auditability and operational-readiness practices for AI solutions.

What you will Bring

AI transformation experience

• Proven experience transitioning a non-AI business area, department, product or operational process into an AI-enabled capability.
• Evidence of delivered or piloted AI solutions that produced measurable business, customer or operational value.
• Experience moving beyond experimentation and addressing adoption, integration, governance and production-readiness challenges.
• Examples may include:
  • AI-assisted network assurance
  • Predictive fault or capacity management
  • Automated incident diagnosis
  • Self-healing or closed-loop operations
  • AI-enabled customer or operational support
  • Intelligent knowledge-management solutions
  • LLM or agent-based workflow automation

AI and emerging technologies

• Practical understanding of AI/ML concepts and their application in enterprise or telecom environments.
• Experience with one or more of the following:
  • Large Language Models (LLMs)
  • Agentic AI and AI agents
  • NCP
  • Knowledge graphs
  • Retrieval-augmented generation
  • Explainable and governed AI
  • AI evaluation, monitoring and operational controls
• Ability to distinguish viable AI use cases from technology demonstrations with limited business value.

Systems analysis and architecture

• Experience in systems analysis, requirements definition, process modelling or solution design.
• Understanding of distributed, cloud-native and microservices-based architectures.
• Ability to map business problems to data, AI, decision and execution components.
• Familiarity with APIs, data flows, system integration and operational dependencies.
• Knowledge of telecom network management, OSS, orchestration or assurance is strongly preferred.

Telecom and autonomous networks (Not essential but good to have)

• Understanding of network-management domains such as RAN, Transport, Core and Cloud.
• Familiarity with TM Forum, 3GPP, ETSI, Open APIs or multi-vendor environments.
• Awareness of autonomous-network concepts, including closed-loop operations, intent-based management and progressive reduction of manual intervention.

Collaboration and influence

• Ability to work across product management, architecture, systems analysis and distributed development teams.
• Strong communication skills, including the ability to explain AI concepts to non-specialist stakeholders.
• Experience influencing decisions without direct line authority.
• A pragmatic, outcome-focused approach to balancing innovation with delivery, risk and maintainability.

What success looks like

Within the first stages of the role, the successful candidate will be able to:

• Identify and prioritise high-value AI opportunities.
• Define and deliver credible AI proof-of-concepts.
• Demonstrate measurable value from AI adoption.
• Establish a practical path from proof of concept to production.
• Improve alignment between business needs, systems architecture and AI implementation.
• Help teams adopt consistent, reusable and governed AI practices.
• Build stakeholder confidence in the safe and effective use of AI.

Education and experience

• Typically five plus years of relevant professional experience in AI, systems analysis, software, telecom, architecture, product development or digital transformation.
• A degree in computer science, engineering, telecommunications, data science or a related discipline is advantageous.
• Equivalent practical experience and a strong portfolio of delivered AI initiatives will be considered.