Senior AI Engineer

Ruby Labs is a leading tech company that creates and operates innovative consumer products with opportunities across the health, education, and entertainment industries.

• 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
• Deep hands-on experience working with LLM APIs (e.g., OpenAI, Anthropic) in production
• 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
• Experience with AI gateways and model routing solutions, such as OpenRouter
• Solid understanding of Redis and relational databases such as PostgreSQL
• Exceptional ownership mindset and personal responsibility for engineering quality and delivery

Nice to have:

• Experience with AI-centered development tools such as Cursor, Claude Code, Windsurf
• Familiarity with evaluation frameworks including LLM-as-a-judge, RAGAS, or similar
• Experience working in high-pressure startup environments with rapid product iteration cycles
• Experience with MCP (Model Context Protocol), including building MCP servers/clients or designing tool contracts for AI agents
• Experience with edge and serverless runtimes such as Cloudflare Workers and supporting services including KV, Durable Objects, Queues, R2, and D1
• Experience with payments, billing, and checkout flows, or orchestration platforms
• Practical experience fine-tuning models for domain-specific tasks or achieving strict JSON/schema compliance
• Working proficiency in Python for data science, evaluation scripts, or AI tooling

AI Systems Ownership & Feature Delivery:

• 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 / 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

Prompt Engineering & Experimentation:

• 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 to root-cause hallucinations and logic errors

Code Quality & Production Reliability:

• Write clean, scalable, and maintainable TypeScript code across the Next.js / Node.js stack
• Build reliable backend logic for AI systems, with strong error handling, request validation, fallback flows, and predictable behavior 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

Applicants must be located within approximately ± 4 hours of Central European Time (CET) to ensure optimal collaboration and communication during working hours.