About Tribe AI

Tribe transforms Fortune 1000 enterprises into AI-native companies. We map where AI creates billions in value, build it to production, and enable your team to run it. We're not a consulting firm handing you a roadmap — we're builders. We embed senior practitioners shoulder-to-shoulder with our clients, ship real systems, and transfer capability so your team owns what we build.

We partner with OpenAI, Anthropic, and Google — we see their roadmaps before they're public. We know what's possible today, what's coming next month, and when to build custom vs. when new tools make it obsolete. We've been 100% AI-focused since founding in 2019 — not as a practice we bolted on, but as the only thing we've ever done.

About the Role

We're looking for AI engineers who build end-to-end. You'll work alongside Tribe's forward-deployed teams — embedded with enterprise clients, building and shipping production AI systems. The problems are messy, the data is imperfect, the stakes are real, and the goal is always production.

This is a contract role (1099, 20–40 hrs/week) staffed on a project basis. You'll be matched to engagements based on your skills and experience. All candidates go through the same technical assessment we use across Tribe — same bar, same process, regardless of engagement type. Strong performers who are interested in full-time opportunities are encouraged to apply; this pipeline feeds both.

You might be architecting an AI system from scratch one month and debugging auth failures in a brittle enterprise environment the next. You might be building agent frameworks, standing up RAG pipelines, or figuring out how to get a fine-tuned model into production on a client's locked-down infrastructure. The common thread: you build things that actually work in the real world, not just in demos.

What You'll Do

• Build and ship AI systems for Fortune 1000 enterprises — from initial architecture through production deployment
• Work across the AI stack: LLMs, agent frameworks, RAG pipelines, vector databases, model fine-tuning, evaluation, and monitoring
• Navigate real enterprise constraints: messy data, legacy systems, security requirements, brittle infrastructure
• Embed with client engineering teams — accelerating adoption, unblocking integration, and transferring capability
• Make build vs. buy decisions grounded in what's actually possible today and what's coming next
• Collaborate with forward-deployed PMs and strategists to translate business problems into technical approaches that ship

About You

• 5+ years of engineering experience, with proven work shipping AI systems into production
• Strong across the stack — you can architect a system, build it, deploy it, and debug it when things break at 2am
• Deep familiarity with modern AI frameworks and tooling (LangChain, LlamaIndex, Hugging Face, vector DBs, etc.)
• Hands-on with cloud infrastructure (AWS, Azure, GCP) — deployment, orchestration, CI/CD, distributed systems
• Comfortable in ambiguity. You thrive when the problem isn't well-defined and the environment isn't "ideal"
• Client-facing and executive-ready. You're credible with engineers in the trenches and clear with C-suite stakeholders
• Builder mentality — you care about outcomes over process, shipping over ceremony, and winning over optimizing for your next role