Hilbert is building a reasoning engine that must navigate non-deterministic user behavior across data silos. Our demand intelligence platform doesn’t just call APIs. It solves the hard problem of orchestrating multi-step inference over messy, high-stakes enterprise data where deterministic answers don’t exist.

We’re looking for an AI Engineer who can build production-grade AI systems end-to-end, from prototype to pipeline to product, with the ownership and urgency of a startup culture.

This is not a “wire up a prompt chain and move on” role. You’ll own core pieces of the AI stack that power Hilbert’s platform: designing agent architectures, building evaluation systems, and making hard tradeoffs between accuracy, latency, and cost in production. You’ll ship fast while the spec is still evolving, and you’ll explain what you’re building (and why) clearly to the rest of the team. If you think in systems, have opinions about how agentic workflows should actually work, and want to build AI products that drive real enterprise outcomes, we want to meet you.

What you’ll own first: evaluation and testing for our agents

Hilbert’s agents are in production with enterprise customers today. Before we expand what they do, we need to know, reproducibly, when a change makes them better or worse. You’ll design the eval harness, define what “correct” means for a multi-step agent trajectory, build the regression gates that run before anything ships, and turn production failures into test cases. From there, your scope widens to retrieval, orchestration, and execution across the AI stack.

What you’ll do

• Own the evaluation layer for our agents: harnesses, metrics, golden datasets, regression gates, and human-in-the-loop review.
• Architect and implement agent workflows using LangChain, LangGraph, or equivalent, including state memory, routing, tool registries, and recovery paths.
• Own systems from experimentation through production, and operate them once they’re live: tracing, monitoring, latency, cost-per-task budgeting, and on-call for what you build.
• Diagnose real production failures (hallucination, tool misuse, retrieval misses, silent degradation) and turn each one into a durable fix and a test.
• Set the technical standard for agent work at Hilbert: review designs, define patterns others build on, and raise the bar across the team.
• Collaborate closely with the founding team and cross-functional partners, communicating tradeoffs, progress, and technical decisions clearly.
• Make pragmatic engineering decisions under ambiguity: ship, learn, iterat