About us

Ruby Labs is a leading tech company that creates and operates innovative consumer products. We offer a diverse range of opportunities across the health, education, and entertainment industries. Our innovative teams are driving the future of consumer-led products, and we're always looking for passionate individuals to join us. Learn more about our story at: https://rubylabs.com/about-us/

About the role

At Ruby Labs we are looking for a Senior AI Engineer to own and drive the quality, reliability, and evolution of our AI systems in production.

This is a high-ownership role. You will be responsible for end-to-end delivery of major AI features, production stability of AI systems, and data-driven experimentation using tools like Langfuse, Mixpanel and OpenRouter. You’ll work in a modern stack built on Next.js, TypeScript, Node.js, and Redis, collaborating closely with product, growth, data, and billing teams. Increasingly, this includes building agentic, tool-using AI systems — defining clean tool contracts (including MCP-based tools) and orchestrating how AI interacts with internal services and business systems.

Our engineering organization uses a squad-based structure. You will operate within an AI engineering squad, contributing as a senior technical voice and driving engineering quality within your area of the product.

Key Responsibilities

Take complete ownership and deliver major AI engineering features within agreed timelines.
Own AI output quality, structure, and predictability across all user-facing AI interactions.
Design, implement, and maintain output-type-based AI systems, including segmentation, routing, and enforcement.
Ensure consistent output structure and formatting across different LLMs for the same request type.
Integrate and orchestrate multiple LLM providers via OpenRouter, managing model selection, fallback strategies, and cost optimisations.
Design and orchestrate tool-using and agentic AI workflows, defining clean tool contracts (including MCP-based tools), function-calling interfaces, and reliable AI-to-system integrations.
Build and maintain complex, multi-step LLM workflows, including with orchestration frameworks such as LangChain or LlamaIndex, for advanced reasoning, context reuse, and retrieval.
Design and manage production prompt systems with dynamic prompting, context injection, and conditional logic.
Own the deployment and release of LLM experiments, prompt management, and Langfuse-based evaluation pipelines.
Run A/B tests across models, analyse results, and present data-driven impact assessments of AI features and experiments.
Monitor AI system metrics, quality signals, latency, and release health using Langfuse and other observability tools.
Deep-debug complex LLM chains using Langfuse traces, identifying bottlenecks and optimising for cost, latency, and context-window usage, and build output-scoring systems to root-cause hallucinations and logic errors.
Write clean, scalable, and maintainable TypeScript code across the Next.js and Node.js stack.
Build reliable backend logic for AI systems, with strong error handling, request validation, fallback flows, and predictable behaviour in production, including reliable tool execution and AI-to-service integrations.
Ensure high code quality through testing, code reviews, and clear engineering standards.
Monitor, troubleshoot, and improve production performance, reliability, and system health.
Drive maintainability and technical quality through solid architecture, refactoring, and disciplined release practices.

Qualifications

6+ years of backend/full-stack software engineering experience, including production-grade TypeScript/Node.js. Experience with Next.js and/or Python is a plus.
2+ years of experience building AI/LLM systems in production. Less experience may be considered for exceptional candidates.
Deep hands-on experience working with LLM APIs (OpenAI, Anthropic, or similar) in production environments.
Experience with Agentic AI, multi-agent orchestration, tool-based workflows (function calling/tool execution), and/or RAG pipelines, including indexing, retrieval, and re-ranking.
Experience with LLM observability tools such as Langfuse, LangSmith, or similar platforms.
Experience with AI gateways and model routing solutions, such as OpenRouter or equivalent technologies.
Solid understanding of Red

Senior AI Engineer at Ruby Labs