AI Engineer at Ruby Labs

Job Description

Ruby Labs is a tech company with a portfolio of consumer products in health, education, and entertainment (100M+ annual users). We'’re seeking a senior AI Engineer (Node.js / Next.js / TypeScript) to shape our AI infrastructure and drive production-ready LLM experiences. You’ll work in a modern stack, making data-driven decisions around model performance, reliability, and cost.

You’ll own advanced prompt systems, structured outputs, and complex LLM workflows using LangChain or LlamaIndex. Observability, debugging, and evaluation are core to the role, leveraging Langfuse and AI gateways like OpenRouter to continuously improve model quality and operational efficiency. You’ll take full ownership of key AI features from experimentation to live production.

Key Responsibilities:

Advanced Prompt Engineering: Designing complex, dynamic prompt templates with conditional logic and efficiently reusing information and context within prompts to maximize generation quality and reasoning.
Structured Outputs & Schemas: Implementing various response schemes (JSON mode, function calling, Zod/JSON schemas) to ensure AI outputs are predictable and ready for seamless integration into application logic.
Prompt Engineering & Evaluations: Building robust evaluation pipelines and using Langfuse to collect feedback and score the quality of responses in real time.
Tracing & Debugging: Performing deep debugging of complex LLM chains using Langfuse traces to identify bottlenecks and optimize for cost, latency, and context window usage.
AI A/B Testing: Running systematic experiments across different models via OpenRouter (e.g., comparing Claude 3.5 Sonnet vs. GPT-4o) and analyzing results based on quantitative metrics.
Data-Driven Decisions: Making deployment decisions for new prompts or models strictly based on quantitative benchmarks and trace data, rather than intuition.
Output Scoring & Analysis: Developing scoring systems to analyze the “Problem → Solution” chain and identify root causes of hallucinations or logic errors using Langfuse analytics.
Model Performance & Fine-Tuning: Regularly re-evaluating model performance as new architectures emerge and performing fine-tuning when necessary to meet specific domain requirements.

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