Senior AI Engineer

TMTG•Sarasota, FL

1d•Remote

About The Position

You'll be building AI-based features into a platform serving millions of users. 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.

Requirements

5+ years of backend engineering experience in a statically typed language (Go, Java, Kotlin, C#).
Production experience shipping LLM-backed features (retrieval-augmented generation, streaming responses, tool use).
Hands-on experience with the LLM serving stack (routing across multiple model providers, failover, token streaming, cost/usage metering).
Experience building retrieval systems (vector search, embedding pipelines, context assembly, citation-backed answers).
Ability to think in terms of failure modes (hallucination, retrieval misses, provider outages, cost blowouts) and build corresponding instrumentation.
Pragmatic approach to evaluation, with the ability to measure AI answer quality (relevance, safety, source quality) using simple, repeatable tests.
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 Haves

Go (strongly preferred)
Python
Experience with LLM gateways or serving infrastructure (LiteLLM, vLLM, TGI, or similar)
Experience with vector databases (Qdrant, pgvector) and embedding pipelines
Experience with fine-tuning open-weight models (LoRA or full fine-tunes) and associated evaluation discipline
Content moderation or safety tooling experience
Familiarity with Ruby on Rails or React/TypeScript

Responsibilities

Building AI-based features into a platform serving millions of users.
Bringing deep, hands-on LLM-stack expertise.
Standing up the core AI infrastructure for a platform with millions of active users.
Shaping the engineering practices around AI infrastructure as the team grows.
Designing and implementing retrieval-augmented generation, streaming responses, and tool use features.
Managing the LLM serving stack, including routing across model providers, failover, token streaming, and cost/usage metering.
Building retrieval systems, including vector search, embedding pipelines, context assembly, and citation-backed answers.
Developing instrumentation to monitor and catch AI failure modes like hallucination, retrieval misses, provider outages, and cost blowouts.
Implementing pragmatic evaluation methods to measure the quality of AI answers (relevance, safety, source quality).
Designing and owning services end-to-end, from schema to deployment to on-call responsibilities.