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About Nimbus
Generic chatbots are a dime a dozen; we’re building the brain behind the machine.
Nimbus is an advanced, modular AI intelligence platform designed to move beyond "chat" and into the realm of complex multi-agent orchestration. We’ve built a sophisticated ecosystem, merging a compiled swarm runtime with a dynamic Client Context Ontology (Neo4j), to turn raw data into autonomous decision intelligence.
At Nimbus, we don’t just provide answers; we provide a collaborative workspace where humans and AI agents solve high-stakes enterprise problems through traceable, beautiful, and deeply intuitive visualisations. We are building the future of how work actually gets done.
The Role
We’re looking for a Full Stack Engineer (Contract-to-Hire) who treats code as a craft and architecture as an art form.
About
This isn't just about "building features", it’s about pioneering the end-to-end experience of a platform that lives at the edge of AI capability. You won't just be connecting APIs; you’ll be:
- Architecting the Core: Building resilient, event-driven services on GCP that power our agentic workflows.
- Defining the Language: Designing robust schema contracts that allow agents and humans to communicate seamlessly.
- Crafting the Visual Frontier: Building a "high-craft" frontend using Vue 3 and VueFlow. You’ll be responsible for making complex graph- and canvas-heavy workflows feel fluid, responsive, and lets face it, magical.
- Bridging Logic & Interface: Implementing how task agents interact with Knowledge Graphs and RAG systems, ensuring that even the most complex AI "thought process" is transparent and intuitive for the user.
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Key Responsibilities
- Platform Feature Engineering: Develop and maintain the Workflow and Perception Engines, and continual learning touchpoints as they land in the codebase, working across catalog-style workflow definitions, runtime integration, and observability.
- AI Implementation: Partner with research on agent tool usage, causal / graph-grounded interactions, and new workflow node patterns. Comfortable with debugging agent loops (tools, retries, grounding failures) end-to-end.
- Retrieval and Vectors: Design and operate embedding + chunking pipelines, vector store usage (e.g. per-tenant collections), metadata for retrieval, and hybrid strategies: vector similarity + keyword + graph expansion where the product requires it.
- API & Schema Design: Design and implement clear contracts between GCP backend (Cloud Run, Pub/Sub, Firestore) and the workspace frontend (GraphQL) where Neo4j / GraphQL surfaces apply.
- Frontend Excellence: Build the collaborative workspace with Vue 3 and VueFlow, emphasising complex state and long-lived sessions, data density appropriate to enterprise (tables, charts, boards) without sacrificing clarity, performance on large graphs and lists (virtualisation where needed), accessibility as a default for customer-grade software.
- Data Pipelines: Build and optimise sophisticated systems to capture granular decision traces and usage signals (e.g. BigQuery), facilitating rapid model and product evolution based on authentic human–agent interactions.
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Technical Requirements
- Experience: 5+ years of professional full-stack development experience, ideally within technical B2B SaaS or API-first environments.
- Backend expertise: Deep understanding of modern cloud architectures, specifically microservices, serverless compute, and event-driven systems utilising Pub/Sub or similar message buses.
- Frontend Proficiency: Strong experience with Vue 3 (or similar frameworks) and a proven ability to manage complex state and interactive flows (e.g. VueFlow).
- Database Knowledge: Practical experience with NoSQL (Firestore) and Graph Databases (Neo4j).
- AI/ML Literacy: Proficient in implementing LLMs, autonomous agents, and tool calling, alongside RAG and prompt + evaluation workflows; keen interest in the technical tradeoffs of embedding models and vector store implementations.
Nice to Have
- Experience with AI orchestration frameworks like LangChain, LlamaIndex, or the Vercel AI SDK.
- Familiarity with data orchestration tools (e.g. Restate) or complex analytics in BigQuery.
- Direct experience in Google Cloud Platform (GCP) ecosystem.
- A background in Data Science or AI research.
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