Senior AI Engineer
Ruby Labs Ltd
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: 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 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 Qualifications
• 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 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 OpenRoute r or equivalent technologies.
- Solid understanding of Redis and relational databases, such as PostgreSQL .
- Exceptional ownership mindset and personal responsibility for engineering quality and delivery.
- Experience with AI-centered development tools such as Cursor, Claude Code, Windsurf , or similar platforms.
- Familiarity with evaluation frameworks, including LLM-as-a-judge, RAGAS, or similar approaches.
- 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.
- Remote Work Environment: Embrace the freedom to work from anywhere, anytime, promoting a healthy work-life balance.
- Unlimited PTO: Enjoy unlimited paid time off to recharge and prioritize your well-being, without counting days.
- Paid National Holidays: Celebrate and relax on national holidays with paid time off to unwind and recharge.
- Company-provided MacBook: Experience seamless productivity with top-notch Apple MacBooks provided to all employees who need them.
- Flexible Independent Contractor Agreement: Unlock the benefits of flexibility, autonomy, and entrepreneurial opportunities. Benefit from tax advantages, networking opportunities, reduced employment obligations, and the freedom to work from anywhere. Read more about it here:
- Recruiter Screening (40 minutes)
- Technical Interview (60 minutes)
- Technical Interview (60 minutes)
- Final Interview (30 minutes)
Vacancy posted 3 days ago
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