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Niche LLM/agent skillset and non-metro location reduce applicant density.
Strong LLM/agent specialization creates high domain bias, limiting cross-industry portability.
Explicit 3-year minimum and required LLM experience create high filter strictness.
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Design, build, and operate multi-agent AI pipelines that execute real business logic such as procurement workflows, supply chain interventions, and autonomous reporting.
Develop agent graphs using frameworks like LangGraph, AutoGen, or CrewAI, incorporating LLM-based tool use and function calling.
Integrate agents with live enterprise data systems and handle task decomposition, memory architectures, and failure recovery in long-horizon autonomous workflows.
Minimum 3 years of hands-on engineering experience.
At least 1 year experience working with LLM-based systems.
Proficiency with Python and experience building multi-agent systems using agent graph frameworks such as LangGraph, AutoGen, or CrewAI.
Work Experience Required: Minimum 3 years engineering experience including 1 year with LLM systems.
Has practical experience designing and deploying multi-agent AI workflows tied directly to business operations (e.g., procurement, supply chain).
Comfortable working with agent protocols like Agent-to-Agent (A2A) or Model Context Protocol (MCP).
Skilled in orchestrating complex autonomous workflows with robust failure handling and agent memory management.