This role sits across platform engineering, ML/LLMOps, IT infrastructure, finance and procurement teams. Success will involve improving cost attribution, utilisation and efficiency across AI workloads while establishing consistent FinOps practices and reporting for projects, teams and applications.
The role provides technical authority across AI platform strategy, foundation models, agentic AI, model lifecycle management, AI engineering frameworks and platform capabilities. It works closely with Enterprise Architecture, AI Platform Engineering, AI Platform Operations, Cybersecurity, AI Solution Architects and business stakeholders. Success means continuously evolving an interoperable, reliable and enterprise-ready AI platform while providing technical direction to architects and engineers.
Successful applicants may be assigned multiple project responsibilities with project team on large projects in commercial, residential, healthcare, infrastructure etc
Run the template against multiple countries in sequence, adapting for language, data source variability and regulatory differences while keeping the core architecture unchanged.
Craft and refine executive messaging, briefing materials, op-eds, press statements, and thought leadership content that articulate Meta's AI vision and innovation agenda to external audiences
Steer artificial-intelligence delivery specifically. You agree stop, pivot and scale criteria with the sponsor before a proof of concept starts. A solution moves to production only when evaluation results meet the agreed threshold and the model-risk and data-privacy sign-offs are in place. You also design human review steps with the client's process owners.
Work within established AI-native engineering workflows, collaborating with AI agents across implementation, testing, debugging, and documentation while validating every output.