Platform Engineer, AI Enablement

Design, build, and operate infrastructure that enables safe and cost-effective access to AI models and tools.

Build the runtime, services, and developer tooling used to run agentic workflows in production.

Create observability across cost, performance, reliability, and usage through metrics, tracing, and logging.

Implement governance and safety controls that make AI use secure, compliant, and auditable.

Develop reusable platform primitives, integrations, and guardrails that reduce one-off solutions.

Contribute to standards and best practices for designing, building, and deploying agents.

Automate infrastructure and deployment processes to keep the platform reliable and scalable.

Partner with Security, IT, and engineering teams on platform integration, policy, and rollout.

Help teams adopt the platform by troubleshooting issues, documenting patterns, and sharing what works.

Educate colleagues on the AI platforms and tools available to them and how to use them effectively.

Own projects end to end and improve the platform based on real-world usage and feedback.

Participate in the team’s on-call rotation, providing out-of-hours support for critical systems when required.

You’re a hands-on engineer who enjoys building reliable platforms for other teams. You’re comfortable navigating ambiguity, taking ownership across the full delivery lifecycle, and collaborating with technical and non-technical partners. You combine strong engineering fundamentals with curiosity about the fast-evolving AI tooling ecosystem.

3+ years of software engineering experience building APIs, services, and developer tooling.

Strong system-design fundamentals and experience making infrastructure decisions that affect multiple teams.

Experience building and operating platforms or infrastructure used by other engineering teams.

Hands-on experience with cloud infrastructure, Kubernetes, and infrastructure as code.

Familiarity with LLM APIs and the surrounding ecosystem, including model providers, gateways, MCP, agent frameworks, and retrieval-augmented generation.

Experience building observability into distributed systems using metrics, tracing, and logging.

A security-conscious approach, including experience with authentication, authorization, and auditability.

A strong sense of ownership and comfort working in an ambiguous, fast-moving environment.

Clear communication and documentation skills that enable other teams to self-serve.

Experience operating an LLM gateway or model-routing layer in production, including cost controls and budget enforcement.

Experience with agent orchestration frameworks or tool-calling protocols such as MCP.

Exposure to AI governance, model risk management, or responsible AI practices.

Experience working in an organisation with a strong safety culture, ideally in autonomy, robotics, or another safety-critical field.

Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know.

We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply. At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law.

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