Jobs at Hilbert's AI

Build and operate production-grade agentic AI systems, owning agent evaluation, testing, retrieval, orchestration, monitoring, and production reliability. Design workflows with LangChain or LangGraph, create evaluation harnesses and regression gates, diagnose failures, optimize latency and cost, and establish technical standards. The role requires 6+ years of software engineering experience, including 2+ years building production LLM or agent systems, plus strong ownership and communication in an ambiguous startup environment.

Build and operate production-grade agentic AI systems, owning evaluation harnesses, metrics, regression gates, retrieval, orchestration, monitoring, and production reliability. Diagnose failures such as hallucinations, tool misuse, and retrieval misses, turning them into durable fixes. The role requires designing LangChain or LangGraph workflows, optimizing accuracy, latency, and cost, integrating external platforms, and collaborating clearly in a fast-moving startup environment.

Build and deploy hybrid data integrations for enterprise customers: implement ClickHouse or warehouse-native pipelines (Snowpark/BigQuery), construct AI semantic layers and canonical growth models, partner with AI discovery agents to accelerate onboarding, and lead technical enterprise deployments ensuring performance and reasoning consistency.

Build and own end-to-end product surfaces for an AI agent: frontend React dashboards, backend agentic query engines, enterprise-ready services, admin tools, and integrations. Collaborate with Product and ML teams to design, ship, and operate scalable, secure, configurable systems.

Lead customer integrations by building warehouse-native or managed ClickHouse pipelines, construct semantic metadata layers, canonicalize source data into unified growth models, and act as the technical lead for enterprise deployments while partnering with AI discovery agents to accelerate onboarding and mapping.

Design, build, and maintain production-grade AI systems and agent-based workflows; own end-to-end pipelines from experimentation to deployment and monitoring; build evaluation pipelines; collaborate with product, data, and GTM; iterate quickly under ambiguity to shape the AI stack.