Platform AI/Model Engineer

As a Platform AI / Model Engineer, you will be part of the AI Center of Excellence (AI COE) – a centralized platform organization responsible for building, operating, and evolving enterprise-grade AI platforms consumed by multiple internal engineering and delivery teams. The AI COE operates as a platform product team, owning shared AI capabilities end-to-end, including AI enablement, governance, observability, and developer tooling. In this role, you will build and operate the model platform and AI enablement layer, with a strong focus on model lifecycle management, AI governance, and the AI Model Gateway that controls access to LLMs and AI services.

What will you do?

AI Model Platform & Lifecycle Management

• Operate and evolve shared services for end-to-end model lifecycle management.
• Design and operate standardised onboarding and offboarding workflows for models, including approval, validation, publication, and retirement.
• Maintain model registries, catalogues, and metadata to support versioning, lineage, and discoverability.
• Shortlist, evaluate, and manage AI models across their lifecycle based on use-case suitability and platform standards.

AI Gateway, Governance & Safety

• Help own and operate the AI Model Gateway, governing access, routing, quotas, and usage of LLMs and AI services.
• Implement and maintain AI safety guardrails, policies, and usage controls aligned with responsible AI principles.
• Collaborate with AI architects and engineers to define and enforce AI governance and platform standards.

Deployment, Runtime & Operations

• Be deeply involved in model deployment and runtime enablement, ensuring models are production-ready, reliable, and operable.
• Monitor and guide performance, scaling, and capacity planning for model serving to ensure cost-effective and reliable operations.
• Support AI/ML systems running in production environments, including troubleshooting and continuous improvement.

Platform Enablement & Collaboration

• Contribute clear documentation, standards, and onboarding materials for AI platform consumers.
• Work closely with internal engineering teams as platform users, balancing enablement with control and safety.
• Take long-term ownership of platform quality, usability, and evolution rather than one-off project delivery.