AI Engineer

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 an AI Engineer (Strong Middle / Senior level) 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 AI features, production stability of AI systems, and data-driven experimentation using tools like Langfuse and Cloudflare AI gateway for model routing. Increasingly, this includes agentic, tool-using AI systems: defining clean tool contracts (including MCP-based tools) and orchestrating how AI interacts with internal services, search and background pipelines.

The stack is TypeScript end to end: Node.js and Hono services on Cloudflare Workers with Durable Objects and Containers, PostgreSQL, OpenSearch. Models from several providers run behind an AI gateway, with prompts, traces and evaluations in Langfuse.

KEY RESPONSIBILITIES

AI Systems Ownership & Feature Delivery

• Take ownership of major AI features and LLM workflows and deliver them to production within agreed timelines.
• Own AI output quality, structure and predictability across user-facing AI interactions, with consistent structured output across different LLMs for the same request type.
• Integrate and orchestrate multiple LLM providers through an AI gateway, managing model selection, fallback strategies and cost optimisation.
• Design and orchestrate tool-using and agentic AI workflows: clean tool contracts (including MCP-based tools), function-calling interfaces and reliable AI-to-system integrations.
• Build and maintain multi-step LLM workflows and RAG pipelines (indexing, embeddings, retrieval, re-ranking) for reasoning, context reuse and search.

Prompt Engineering, Evaluation & Experimentation

• Design and manage production prompt systems with dynamic prompting, context injection, and conditional logic.
• Own prompt management, the release of LLM experiments and Langfuse-based evaluation pipelines: test datasets, LLM-as-a-judge, output scoring.
• Run A/B tests across models and prompts, analyse the results and present the data-driven impact of AI features and experiments.
• Deep-debug LLM chains using Langfuse traces: identify bottlenecks, optimise for cost, latency and context-window usage, and root-cause hallucinations and logic errors.

Code Quality & Production Reliability

• Write clean, scalable and maintainable TypeScript across the Node.js / Cloudflare Workers stack.
• Build reliable backend logic for AI systems: request and output validation, error handling, fallback flows for malformed or low-quality model responses, and reliable tool execution.
• Ensure high code quality through tests (including validation and fallback paths for AI output), code reviews and clear engineering standards.
• Monitor AI system metrics, quality signals, latency and release health, and resolve production issues with a documented root cause.
• Document prompt designs, model and routing choices, evaluation results and architectural decisions, so others can pick up the work.

QUALIFICATIONS

• 4+ years of backend/fullstack software engineering experience, with production-grade TypeScript/Node.js.
• 1+ year of AI/LLM systems in production with real users, not only side projects or experiments.
• Daily use of AI-centred development tools (Claude Code, Cursor or similar).
• Deep hands-on experience with LLM APIs (OpenAI, Anthropic, Google Gemini or similar) in production: structured output, function calling, streaming, token and cost control.
• Experience with agentic AI, multi-step orchestration, tool-based workflows (function calling / tool execution) or RAG pipelines (indexing, retrieval, re-ranking).
• Experience with LLM observability and evaluation tools (Langfuse, LangSmith or similar).
• Experience with AI gateways and model routing (OpenRouter, Cloudflare AI Gateway or equivalent).
• Solid understanding of relational databases (PostgreSQL) and caching (Redis, KV or similar).
• Exceptional ownership and personal responsibility for engineering quality and delivery.

NICE TO HAVE

• Evaluation frameworks (LLM-as-a-judge, RAGAS) and building test datasets.
• MCP (Model Context Protocol): building MCP servers/clients or designing tool contracts for AI agents.
• Edge/serverless runtimes (Cloudflare Workers) and their services: KV, Durable Objects, Queues, R2, Containers.
• Search engines and vector search (OpenSearch, Elasticsearch, pgvector), embeddings.
• Background workflow engines (Hatchet, Temporal, Inngest) for high-volume LLM …