Senior AI Engineer

About this role

About Crexi

Crexi is reimagining commercial real estate with an AI-powered platform built to deliver smarter, more efficient solutions at every stage of the deal lifecycle. From real-time data and market insights generated by Crexi Intelligence, to targeted property marketing and seamless deal management through Crexi PRO, and a transparent, time-bound bidding experience with Crexi Auction— Crexi enables users to evaluate opportunities, maximize exposure, and close with speed and confidence. To date, Crexi has facilitated over $1 trillion in transactions, 8.6 billion square feet leased, and supports a growing community of more than 2 million monthly active users.

Crexi’s mission is to catalyze the next generation of commercial real estate through three core pillars: Access, Innovation, and Connection. Crexi’s platform democratizes CRE by providing unprecedented access to market insights and opportunities, accelerates CRE dealmaking with purpose-built technology that enhances speed and transparency; and empowers CRE professionals with a centralized platform designed for real-time collaboration and success.

About the Role:

The AI Engineer builds the agentic AI systems that power Crexi's platform, including orchestration, retrieval, and action layers grounded in Crexi's proprietary commercial real estate data. The role builds multi-step agentic workflows that understand user intent, orchestrate the right capabilities, and complete real work, not just answer questions, using tools like LangGraph and AWS Bedrock AgentCore. It exists to extend Crexi's AI-powered research, document generation, and zoning intelligence into unified, trustworthy agentic experiences for brokers, appraisers, lenders, and investors.

What You'll Do:

A typical day may include:

• Design and implement multi-step agentic workflows, including routing, planning, tool use, and state management, using frameworks like LangGraph/LangChain to move from user intent to real, completed actions.

• Build evaluation frameworks and production monitoring (offline evals, human-in-the-loop annotation) to measure quality, reliability, and business outcomes, not just model metrics.

• Build context and retrieval pipelines over Crexi's proprietary CRE data, handling the compound, qualitative queries that structured filters can't answer.

• Work primarily with Anthropic frontier models on AWS Bedrock, using Bedrock AgentCore for agent runtime, identity, and memory, and helps evaluate open-weight alternatives on cost, latency, and capability.

• Implement guardrails, structured outputs, and graceful ambiguity handling so agentic systems ask for clarification rather than acting on unclear intent, and establishes trace-level observability across LLM calls and tool invocations.

• <span data-contrast="none"