multiverse - Staff AI Engineer - AI Transformation

• Production AI Agent Engineering • You have shipped multi-agent systems or complex AI products to real users. You understand the engineering challenges that make agent systems a distinct discipline: • Context management. Designing what enters the context window and what stays out. Retrieval strategies, chunking, conversation memory, summarisation, and the cost/quality trade-offs of each. You have made these decisions in production and seen the consequences. • Model selection and routing. Choosing the right model for each task based on capability, latency, cost, and reliability. Building routing logic that matches work to the appropriate model rather than defaulting to one. • Cost engineering. Token economics, caching, prompt optimisation, batching. You know the difference between a prototype that works and a production system that works at sustainable cost. You have built systems where cost was an engineering constraint, not someone else's problem. • Tool use and agent augmentation. Designing what capabilities agents can reach: tool descriptions that models use reliably, failure handling, MCPs or equivalent interfaces. You understand that the quality of the tool layer determines whether agents are useful or fragile. • Multi-agent coordination. Task decomposition across agents, handoff protocols, shared state, orchestration logic. You have built systems where multiple agents work together within a product domain and understand the architectural patterns that make coordination reliable. • Evaluation and quality. Building eval frameworks for AI output: accuracy, helpfulness, safety, domain-specific criteria. Automated pipelines and human-in-the-loop review. You would not ship an agent system without a quality baseline. • Product Thinking and Entrepreneurial Instinct • On a small squad there is no gap between product thinking and engineering. You own the problem from user need to production system. You can sit with the people whose work you are transforming, understand their workflow, identify the highest-value intervention, and build it without waiting for a product manager to write a spec. • You have either built something yourself (a product, a startup, a project with real users) or operated with that founder mindset inside a larger organisation. You understand that speed matters and that shipping something useful beats polishing something theoretical. • AI-Native Engineering • You build with Claude Code daily. You set context and constraints before generating code. You review AI output critically. You augment the tool with skills, system prompts, and domain context to make it effective. This is how the team works, and you help define what good looks like. • Full-Stack Delivery • You work across the stack: LLM integration, backend services, data pipelines, and enough frontend to ship end to end. The boundaries between these layers dissolve in agent systems, and so should your willingness to work across them. • You can explain technical strategy to a CPO, walk a product manager through a cost trade-off, and give direct feedback in code review. You represent the team's technical approach in cross-functional forums with product, design, learning design, compliance, and other engineering teams. You document decisions, not just code. • WHAT WOULD SET YOU APART • Experience in EdTech, regulated content, or domains where AI output quality has compliance or accreditation implications • Background as a founding engineer or technical co-founder • Published thinking or external contributions in AI engineering (talks, writing, open source) • Experience designing platform layers that other teams build on • Practical experience with MCP (Model Context Protocol) or equivalent agent integration standards • WHAT WE ARE NOT LOOKING FOR • Pure ML research without production engineering experience. We need builders • Narrow specialism. This team works across the full stack of an AI product. If you only do infrastructure, or only do model training, or only do frontend, this is the wrong fit • People who need a detailed spec, a sprint plan, and a standup before they can write a line of code. We ship fast and iterate • Candidates whose experience is limited to wrapping LLM APIs in thin application layers. We need depth in agent architecture, context strategy, tool design, and multi-agent coordination • Engineers who optimise for technical elegance over user outcomes. The architecture serves the product