Senior AI Engineer

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-us/About the role

We are looking for a Senior AI Engineer to own and scale the AI layer of one of our core D2C subscription products. You will be fully responsible for production LLM features and the browser automation built around them end to end - prompt systems, structured outputs, evaluation, observability, and model selection - from first experiment to live release, with quality, reliability, and cost as your accountability.

This is a high-impact role suited for someone who decides with data, not intuition. Unmeasured prompt changes, untraced failures, and model swaps without benchmarks are not acceptable. You work in a modern Type Script stack - Next.js, Cloudflare Workers and Containers with Hono, Hatchet task queues, Drizzle on Supabase Postgres, Vercel Blob. Browser automation on Browserbase and Playwright is roughly half of the job.

Design dynamic prompt templates with conditional logic and context injection that maximise generation quality

Enforce predictable outputs with JSON mode, function calling, Zod/JSON schemas, validators, and repair loops

Own complex LLM workflows (Lang Chain, Llama Index, or custom) that integrate cleanly into application logic

Build and maintain browser automation on Browserbase Functions and Playwright that traverses web flows reliably

Classify screens with a model inside each traversal, and decide what the model handles versus deterministic code

Ship automations as frozen, published workers - versioned, reproducible, and safe to roll back

Orchestrate runs through Hatchet with retries, timeouts, and failure traces that explain every broken run

Build evaluation pipelines - test datasets, LLM-as-a-judge, RAGAS-style metrics - and score responses and screen classifications in Langfuse

Trace complex LLM chains to find bottlenecks in cost, latency, and context-window usage

Run A/B experiments across models and prompts through an AI gateway (Open Router, Cloudflare AI Gateway); release only on quantitative benchmarks

Re-evaluate models as new architectures emerge; fine-tune when domain requirements demand it

Take AI features from experiment to live production on Cloudflare Workers and Containers, and stay accountable after release

Manage rate limits, fallbacks, and model cost so features stay reliable at scale

Resolve production incidents in owned AI and automation systems with documented root-cause analysis

• 【5+】 years of software engineering with Node.js, Type Script, Next.js, and Postgres, including edge or serverless runtimes such as Cloudflare Workers
• Production browser automation with Playwright (Browserbase or similar): resilient selectors, retries, failure handling at scale
• Production experience with dynamic prompting - content that depends on input variables and injected context
• Structured outputs in production: JSON mode, function calling, Zod/JSON schemas
• Experience with unified LLM APIs or AI gateways (Open Router, Cloudflare AI Gateway): rate limits, fallbacks, cost-effective model selection
• LLM observability with Langfuse or similar: tracing, test datasets, scoring integration
• Evaluation methodology: RAGAS or custom LLM-as-a-judge systems

• Decides on benchmarks and trace data, not intuition
• Analytical - converts generation logs into metrics a…