AI/Model Platform Engineer

As an AI/Model Platform Engineer, you will design, build, and operate the internal platforms that enable secure, scalable, and responsible deployment of AI/ML models across the organisation. You will focus on model lifecycle management, governance, safety guardrails, and AI gateway orchestration, working closely with data science, engineering, product, and risk teams to ensure AI models are production-ready, compliant, and reliable.

What will you do?

AI/Model Platform Engineering & Operations

• Design, develop, integrate, and maintain model management and deployment platforms.
• Build and operate model registry systems supporting versioning, lineage tracking, and lifecycle management.
• Deploy and manage AI/ML models in containerised and microservices-based environments.
• Monitor platform performance, reliability, and security; troubleshoot and resolve issues.

AI Governance, Safety & Compliance

• Standardise and automate model card creation to document model performance, usage, and risks.
• Implement safety guardrails, including automated validation, fairness checks, and policy enforcement.
• Support responsible AI practices and align platform capabilities with governance and regulatory requirements.

AI Gateway & Integration

• Design, deploy, and optimise AI Gateway solutions for intelligent routing, request validation, and model selection.
• Enable secure and scalable access to multiple AI endpoints and services.
• Integrate platform components with CI/CD pipelines for ML workflows.

Collaboration, Documentation & Continuous Improvement

• Collaborate with data scientists, engineers, product managers, and risk/compliance teams to translate requirements into technical solutions.
• Analyse process and program specifications and translate them into robust, maintainable code.
• Define and maintain technical documentation, onboarding guides, and best practices.
• Participate in requirements gathering, functional specification reviews, and peer code reviews.
• Stay current with advancements in AI infrastructure, tooling, and industry best practices.