> Markdown version of [/jobs/ext/4629219-senior-backend-engineer-ai-platform](https://www.wearedevelopers.com/jobs/ext/4629219-senior-backend-engineer-ai-platform). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Backend Engineer, AI Platform - **Company:** TMTG Group LLC - **Location:** Sarasota, FL, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Artificial Intelligence, C Sharp (Programming Language), Data Centers, Failover, Python (Programming Language), PostgreSQL, RabbitMQ, Ruby on Rails, Redis, Search Technologies, TypeScript, Qdrant, ReactJS, Large Language Models, Backend, Kotlin, Pgvector, AI Platforms, LiteLLM, Api Design, Golang - **Published:** November 7, 2026 - **Apply:** https://www.juju.com/job/28_25197_175e52d55fd66ae0fc0369c44abc3f12 ## About the Role * 5+ years of backend engineering experience in a statically typed language (Go, Java, Kotlin, C#) * You've shipped production LLM-backed features - retrieval-augmented generation, streaming responses, tool use - and lived with them after launch * Hands-on experience with the LLM serving stack: routing across multiple model providers, failover, token streaming, and cost/usage metering * Experience building retrieval systems: vector search, embedding pipelines, context assembly, and citation-backed answers * You think in failure modes: hallucination, retrieval misses, provider outages, cost blowouts - and you build the instrumentation to catch them * Pragmatic about evaluation - you know how to measure whether AI answers are actually good (relevance, safety, source quality) with simple, repeatable tests, not just academic benchmarks * Strong API design instincts; comfortable owning a service end to end, from schema to deploy to on-call * US-based and authorized to work in the United States Nice to Have * Go (strongly preferred) * Python * Experience with LLM gateways or serving infrastructure (LiteLLM, vLLM, TGI, or similar) * Vector databases (Qdrant, pgvector) and embedding pipelines * Fine-tuning open-weight models (LoRA or full fine-tunes) and the eval discipline that goes with it * Content moderation or safety tooling experience * Familiarity with Ruby on Rails or React/TypeScript (you'll integrate with both) Our Stack Go, Ruby on Rails, Python, React/TypeScript, PostgreSQL, Redis, RabbitMQ. Services deployed across multiple data centers. ## Description You'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. The work is greenfield. You won't be maintaining someone else's pipeline - 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.