Forward Deployed AI Engineer

About Tribe AI

Tribe helps enterprises turn AI from an experiment into infrastructure they can actually depend on.

Large organizations know AI can reshape how they operate, but getting from a promising prototype to a reliable production system is hard. That’s where we come in.

Our engineers work directly with enterprise teams to architect, build, deploy, and improve AI systems in real-world environments. That means dealing with imperfect models, messy data, changing requirements, reliability constraints, and the realities of putting AI in the hands of users.

The Role

We’re looking for a senior, hands-on engineer who knows how to make AI systems work beyond the demo.

As a Forward Deployed AI Engineer, you’ll embed with client teams and take ownership of LLM-powered systems from architecture through production. You’ll work on problems where behavior isn’t perfectly deterministic and where reliability has to be engineered rather than assumed.

You might be improving a RAG system that retrieves the wrong context, debugging an agent that behaves unpredictably, building evaluation infrastructure to catch regressions, or redesigning an architecture that has become too slow or expensive at scale.

This isn’t a research position or a role focused primarily on prompt engineering. You’ll be expected to build, ship, debug, and operate real systems that businesses depend on.

What You’ll Work On

Build and own production AI systems

Design, develop, deploy, and operate LLM-powered applications and services. You’ll make architectural decisions across models, infrastructure, data, reliability, latency, and cost, and continue owning those decisions after launch.

Build reliable RAG and agentic systems

• Develop and improve retrieval pipelines across ingestion, chunking, retrieval, reranking, grounding, and evaluation.
• Design agents that safely interact with tools, APIs, and enterprise data. Debug failure modes such as looping, incorrect tool usage, poor grounding, and hallucinated actions.
• Just as importantly, recognize when an agent isn’t the right solution.

Measure AI behavior

• Build evaluation systems that combine offline testing, production signals, and human feedback.
• Create ways to detect behavioral regressions caused by model changes, prompts, retrieval quality, or shifting data.
• Instrument systems so that when something goes wrong, the team can understand why.

Operate what you build

• Deploy AI systems into real cloud environments with monitoring, CI/CD, versioning, rollout strategies, and rollback mechanisms.
• Manage model and provider changes without treating production like an experiment.
• Keep an eye on economics too. AI systems need to be reliable, but they also need to make financial sense at scale.

Work directly with clients

• You won’t disappear behind a product manager.
• You’ll work with client engineers, technical leaders, product teams, and business stakeholders to turn ambiguous problems into technical decisions and working systems.
• When something breaks, you’ll be expected to understand the problem, communicate it clearly, and lead the path toward a solution. ‍ ### Who This Is For

You’re likely a fit if you:

• Have 5+ years building and operating production systems.
• Have shipped and owned LLM-powered systems in production, not just prototypes.
• Have dealt with hallucinations, retrieval failures, or agent misbehavior beyond prompt tweaks.
• Have built or maintained LLM evaluation pipelines and know their limits.
• Understand model drift, data drift, and behavioral regression in live systems.
• Are strong in Python and comfortable building backend services, pipelines, and workers.
• Have real experience with cloud infrastructure, CI/CD, monitoring, and incident response.
• Can explain to stakeholders why AI systems fail — without hiding behind hype.
• Value intellectual honesty, low ego, and responsibility when things break. ‍ You are not a fit if:
• Your AI experience is mostly API integration or demos.
• You expect prompt engineering alone to solve reliability.
• You haven’t owned AI behavior after launch.
• You want deterministic guarantees before you’re willing to take responsibility.
• You avoid accountability when systems fail in production.

Why Join Us:

Impact: Build AI systems that enterprises actually rely on.
Autonomy: Own delivery end-to-end, not tickets.
Variety: Work across industries and problem types.
Growth: Sharpen both engineering judgment and client-facing instincts.
Culture: High competence, low ego, strong opinions loosely held.
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