Senior AI Engineer
Ruby LabsAbout 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.
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