Build the Future of Perception

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 Serverless Workflow Engine (Mastra AI) 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.

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.

If you’re bored of standard CRUD apps and want to build the infrastructure for the next generation of autonomous intelligence, let’s talk.

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.

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 Mastra AI, LangChain, or LlamaIndex.
• 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.