AI Engineer - Core

Hilbert is building a reasoning engine that must navigate non-deterministic user behavior across data silos — turning months-long decision cycles into minutes. Fully agentic by design, 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.From Fortune 500 enterprises to beloved brands like Fresh Direct, Blank Street, and Levain Bakery, operators run their growth on Hilbert.

We're also co-building alongside leading AI companies. 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 demand intelligence platform — designing agent architectures, building evaluation systems, and making hard tradeoffs between accuracy, latency, and cost in production.

You'll ship fast in conditions where the spec is evolving, and communicate what you're building (and why) with clarity 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. THE ROLE What You'll Do

• Design, build, and maintain AI-driven features and pipelines that serve enterprise customers at scale
• Architect and implement agent-based workflows using Lang Chain, Lang Graph, or equivalent orchestration frameworks
• Own systems end-to-end — from experimentation through production deployment and monitoring
• Build and improve evaluation pipelines to measure, validate, and iterate on AI system performance
• Collaborate closely with the founding team and cross-functional partners — communicating tradeoffs, progress, and technical decisions with clarity
• Make pragmatic engineering decisions under ambiguity — ship, learn, iterate
• Shape the technical direction of the AI stack as the company scales

• Intelligent retrieval across heterogeneous approaches — our agents need the right information at exactly the right moment. The challenge isn't picking one retrieval method; it's combining RAG, graph-based retrieval, and other approaches into a unified strategy that fetches the most relevant content precisely when the agent needs it — no more, no less.
• Agentic workflows that solve real-world problems — it's building workflows robust enough to handle the unexpected. When an agent hits an edge case, missing data, or a situation it wasn't explicitly designed for, it needs to reason through it — leveraging available context, escalating to a human when it can't, and never silently failing.
• Evaluation beyond vibes — we need systematic, reproducible evals that actually predict real-world performance. If you've built custom evaluators for RAG or agent workflows, we want to talk.
• Execution and real-world integration — an agent that only surfaces insights isn't enough. We're building systems where agents take action — integrating with external platforms, executing workflows, and doing real work with the information they have, combined with human-in-the-loop checkpoints that keep enterprise trust intact.

• You're a strong Software engineer. Your code is clean, testable, and production-ready.
• You have real experience with Lang Chain, Lang Graph, or equivalent agent/orchestration frameworks. You've built with them, hit their limits, and worked around them - not…