AI Agent Engineer

AI Agent Engineer Evanston, IL (On-Site)Bear Claw is an AI-powered recruiting and applicant tracking platform built to help recruiters work smarter and place top talent faster. Our platform brings the recruiting workflow into one system—from candidate and job management to sourcing, outreach, and team collaboration. At the center is Cora, our conversational AI assistant, which uses vector search and LLM-powered technology to identify candidate-job matches, surface opportunities, and help recruiters take action faster. We are seeking an experienced AI Agent Engineer to help architect, build, and scale Cora, the AI intelligence layer within our recruiting and Applicant Tracking System (ATS).This is a hands-on engineering role focused on building production-grade agentic AI systems that can understand recruiting workflows, retrieve and reason over structured and unstructured data, use tools, interact with external systems, and take controlled actions within our platform. The ideal candidate has experience moving beyond basic LLM applications and RAG prototypes into production AI agents with orchestration, tool calling, memory, evaluation, observability, and appropriate human controls. What You'll Build Architect and develop agentic workflows that automate and assist complex recruiting processes. Build agents that can reason across candidates, jobs, clients, recruiter activity, communications, and other ATS data. Design tool-calling and MCP-based integrations that allow Cora to safely interact with internal services and external APIs. Build and improve RAG and retrieval systems, including vector search, hybrid search, metadata filtering, reranking, and grounded responses. Develop orchestration for multi-step and multi-agent workflows, including task routing, state management, context sharing, and escalation. Design short- and long-term agent memory strategies where appropriate. Implement guardrails, permissions, validation, and human-in-the-loop controls for agent actions. Build evaluation frameworks for measuring retrieval quality, agent behavior, tool execution, accuracy, latency, and cost. Integrate AI services with our existing AWS infrastructure and application stack. Develop production APIs and services using Python and modern backend frameworks. Monitor, debug, and continuously improve agent performance in production. Work directly with product and engineering leadership to take AI capabilities from concept through production.