Platform Engineer, AI Enablement
wayve · London, England ·
Last confirmed by our partners on 21 Oct 2026.
Overview
As a Platform Engineer on Wayve’s AI Enablement team, you will create and maintain the infrastructure that provides safe, reliable, and cost-efficient access to language models and AI agents. You will build a governed model-access layer and a secure production environment for agentic workflows, with safety and observability built in.
Your work covers design, implementation, deployment, and ongoing operation in close collaboration with Security, IT, and engineering teams. This role offers ownership and visibility across Wayve’s infrastructure, shaping how AI tools are used responsibly at scale.
You will join an early platform team and help others adopt the platform through reusable patterns,–
Responsibilities
- Design, build, and operate infrastructure enabling safe, cost-effective access to AI models and tools
- Develop runtime, services, and developer tooling for production agentic workflows
- Create observability (metrics, tracing, logging) across cost, performance, reliability, and usage
- Implement governance and safety controls ensuring secure, compliant, auditable AI use
- Develop reusable platform primitives, integrations, and guardrails
- Contribute to standards and best practices for agents deployment
- Automate infrastructure and deployment processes for reliability and scalability
- Partner with Security, IT, and engineering on platform integration and rollout
- Assist teams in adopting the platform through troubleshooting, pattern documentation, and knowledge sharing
- Educate colleagues on available AI platforms and tools
- Own projects end-to-end and improve the platform based on real-world feedback
- Participate in on-call rotation for critical systems
Key requirements
- 3+ years of software engineering experience building APIs, services, and developer tooling
- Strong system-design fundamentals and experience making cross-team infrastructure decisions
- Experience building and operating platforms or infrastructure used by multiple engineering teams
- Hands-on experience with cloud infrastructure, Kubernetes, and infrastructure as code
- Familiarity with LLM APIs and the surrounding ecosystem (model providers, gateways, MCP, agent frameworks, retrieval-augmented generation)
- Experience adding observability to distributed systems (metrics, tracing, logging)
- Security-conscious approach including authentication, authorization, and auditability
- Ownership mindset and ability to work in ambiguity and fast-moving environments
- Clear communication and documentation skills enabling self-serve for other teams
- hands-on mindset
- ability to navigate ambiguity
- strong collaboration with technical and non-technical partners
- APIs, services, and developer tooling
- Cloud infrastructure
- Kubernetes