You won't be maintaining someone else's pipeline \u2014 you'll be standing up the core AI infrastructure for a platform with millions of active users, and shaping the engineering practices around it as the team grows.\n \nWhat We're Looking For\n\n 5+ years of backend engineering experience in a statically typed language (Go, Java, Kotlin, C#)\n You've shipped production LLM-backed features \u2014 retrieval-augmented generation, streaming responses, tool use \u2014 and lived with them after launch\n Hands-on experience with the LLM serving stack: routing across multiple model providers, failover, token streaming, and cost/usage metering\n Experience building retrieval systems: vector search, embedding pipelines, context assembly, and citation-backed answers\n You think in failure modes: hallucination, retrieval misses, provider outages, cost blowouts \u2014 and you build the instrumentation to catch them\n Pragmatic about evaluation \u2014 you know how to measure whether AI answers are actually good (relevance, safety, source quality) with simple, repeatable tests, not just academic benchmarks\n Strong API design instincts; comfortable owning a service end to end, from schema to deploy to on-call\n US-based and authorized to work in the United States\n \nNice to Have\n\n Go (strongly preferred)\n Python\n Experience with LLM gateways or serving infrastructure (LiteLLM, vLLM, TGI, or similar)\n Vector databases (Qdrant, pgvector) and embedding pipelines\n Fine-tuning open-weight models (LoRA or full fine-tunes) and the eval discipline that goes with it\n Content moderation or safety tooling experience\u2022 Familiarity with Ruby on Rails or React/TypeScript (you'll integrate with both)\n \nOur Stack\n \nGo, Ruby on Rails, Python, React/TypeScript, PostgreSQL, Redis, RabbitMQ. You'll be building AI-based features into a platform serving millions of users.\n \nAbout the Role\n \nYou'll work directly with the platform architect and other developers to bring deep, hands-on LLM-stack expertise: you've built these systems before, you know where they break, and you know what "good" looks like in production.\n \nThe work is greenfield.