Lead the engineering, operationalisation and lifecycle management of AI capabilities used across Network Operations, ensuring models are trusted, reliable and scalable.
Responsibilities
Own and evolve the Operational AI Engineering and Model Lifecycle strategy, capability roadmap and engineering direction across Network Operations.
Translate operational priorities into a prioritised portfolio of AI engineering capabilities aligned to reliability, resilience, productivity and customer outcomes.
Lead the engineering and industrialisation of production-ready AI capabilities, including feature engineering, model integration and deployment patterns.
Establish reusable AI engineering standards and components that enable consistent, scalable delivery across network and service domains.
Own the lifecycle of operational AI models, from production acceptance through monitoring, retraining, optimisation and retirement.
Own the product experience across site readiness, building-system integration, data collection, configuration, commissioning, validation, training, and customer handoff.
Compensation
Competitive salary based on experience
Responsibilities
Own the roadmap for deployment, implementation, and customer onboarding
Map and improve the customer journey from contract signing through go-live
Work with customers, facility teams, implementation specialists, and Engineering to identify deployment challenges
Improve how building, equipment, and operational data is collected, mapped, validated, and configured
Build tools and workflows that reduce manual effort and improve deployment predictability
Provide the critical link between production strategy and operational delivery, ensuring maintenance priorities, asset health requirements, technical support and resource capability are aligned to improve production performance and resilience.
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