About the company
We are looking for a Senior AI Platform Engineer to design, build, and operate a production-grade AI platform within a complex enterprise environment.
In this role, you will take end-to-end technical ownership of the AI platform, including AI gateway engineering, governance, agent management, access control, cost telemetry, observability, and developer enablement. This is a highly hands-on role for someone who has already built an AI governance/platform capability in production and can bring that experience into a new environment.
Responsibilities
- Build and operate an enterprise AI Gateway, including SSO, request logging, data-classification tagging, policy enforcement, and model routing.
- Work with Azure OpenAI / AI Foundry, Entra ID, and Azure API Management (APIM).
- Build and maintain a governed repository of approved AI agents, including ownership, permissions, and data scope.
- Develop cost telemetry to attribute AI usage to specific cost centres and use cases.
- Automate access provisioning and licence lifecycle management for enterprise AI tools.
- Develop reusable developer templates and golden paths for RAG, AI agents, and evaluation.
- Establish production observability, monitoring, and operational runbooks.
- Build and operate containerised workloads on Kubernetes.
- Implement identity and access controls including SSO, RBAC, and service principals.
- Build internal platform capabilities and developer services that can be adopted across engineering teams.
- Implement Infrastructure as Code and CI/CD practices for reliable platform delivery.
Requirements
- Previous hands-on experience building an AI governance/platform capability end-to-end in production.
- Ability to demonstrate a previous AI platform implementation, including what was built, what it governed, who used it, and lessons learned.
- Strong production experience with Kubernetes and containerised workloads.
- Hands-on experience with AI gateways / LLM proxies, such as APIM, Kong, LiteLLM, or equivalent.
- Practical experience developing LLM applications, including API integration, token/cost behaviour, evaluation, and RAG patterns.
- Strong production observability experience using OpenTelemetry, Grafana, Azure Log Analytics, or equivalent.
- Strong backend engineering skills in Go (preferred), Python, or TypeScript.
- Hands-on experience with Infrastructure as Code and CI/CD, including Terraform and GitHub Actions or Azure Pipelines.
- Strong experience with identity and access management, including SSO, RBAC, and service principals.
- Proven experience building an internal platform or developer service successfully adopted by other teams.
- Strong ownership, problem-solving, and independent working capabilities.
Conditions
- Build and shape enterprise-scale AI platform capabilities.
- Work hands-on with modern Azure and Generative AI technologies.
- Take ownership of technically complex and business-critical solutions.
- Work across AI engineering, cloud, platform engineering, security, and DevOps.
- Contribute to the adoption of AI across large-scale enterprise environments.