8 hours ago
London, United KingdomMid Level
Responsibilities
- Design, build, and operate infrastructure for safe and cost-effective access to AI models and tools.
- Build production runtimes, services, and developer tooling for agentic workflows.
- Implement metrics, tracing, and logging for cost, performance, reliability, and usage observability.
- Develop governance, safety, security, compliance, and auditability controls for AI use.
- Create reusable platform primitives, integrations, guardrails, and deployment automation.
- Define standards and best practices for designing, building, and deploying agents.
- Partner with Security, IT, and engineering teams on platform integration, policy, and rollout.
- Support platform adoption through troubleshooting, documentation, reusable patterns, and colleague education.
- Own projects end to end and participate in the team’s on-call rotation for critical systems.
Requirements
- 3+ years of software engineering experience building APIs, services, and developer tooling.
- Strong system-design fundamentals and experience making infrastructure decisions affecting multiple teams.
- Experience building and operating platforms or infrastructure used by engineering teams.
- Hands-on experience with cloud infrastructure, Kubernetes, and infrastructure as code.
- Familiarity with LLM APIs, model providers, gateways, MCP, agent frameworks, and retrieval-augmented generation.
- Experience implementing observability in distributed systems using metrics, tracing, and logging.
- Security-conscious experience with authentication, authorization, and auditability.
- Strong ownership, communication, documentation, collaboration, and comfort working in ambiguity.
- Preferred: experience operating production LLM gateways or model-routing layers with cost controls and budget enforcement.
- Preferred: experience with agent orchestration frameworks or tool-calling protocols such as MCP.
- Preferred: exposure to AI governance, model risk management, responsible AI, or safety-critical engineering environments.
Tech Stack
Categories
About Wayve
Wayve builds end-to-end autonomous driving software—the vehicle-agnostic Wayve AI Driver—that runs on onboard compute and native sensors, licensed to automakers and fleet operators. Its platform spans ADAS and higher autonomy (L2+/L3 to robotaxi) and is designed to generalize across vehicle types and geographies. Founded in 2017 and headquartered in London, it tests its models across Europe, North America, and Japan, with a U.S. base in Sunnyvale, CA.