Remote Agentic AI Engineer (based in US) - tribe.ai - Anywhere

About Tribe AI:

At Tribe, we’re on a mission to help enterprises rearchitect their business with AI. Today, every large enterprise wants to transform its business with AI, but they often lack the capabilities to do so. At Tribe, this gap is our opportunity.

We embed senior engineers to design, ship, and operate AI systems where correctness is probabilistic, failure modes are subtle, and success is measured by adoption, stability, and business outcomes.

About the Role

This is a forward-deployed, hands-on engineering role for people who’ve lived through multiple technology waves and know the difference between what’s exciting and what survives production.

You’ll embed with enterprise teams to build and harden LLM-powered systems under real constraints: hallucinations, retrieval failures, agent misbehavior, silent regressions, cost explosions, and shifting data distributions. You own systems end-to-end, from architecture through post-launch behavior.

This is not a research role, a prompt-only role, or a feature factory.

It is a delivery role for engineers who can make stochastic systems behave well enough that businesses rely on them.

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Examples of what You’ll Own:

Own production AI systems end-to-end
• Design, build, deploy, and operate LLM-powered systems in production.
• Own reliability for probabilistic systems: hallucinations, grounding, drift, latency, and cost.
• Make tradeoffs explicit when correctness, speed, and cost are in tension.

LLM, RAG, and/or Agentic Systems experience
• Build and debug RAG pipelines: ingestion, chunking, retrieval quality, reranking, grounding, and evaluation.
• Design agent workflows that interact with tools, APIs, and data without looping, stalling, or hallucinating authority.
• Identify when agents are the wrong abstraction and kill them early.

Evaluation, Observability, and Drift
• Build and maintain evaluation frameworks for LLM outputs (offline, online, human-in-the-loop).
• Detect silent regressions caused by model