Ai Agent Architect
SimpleCiti Companies, Garden City, ID, United States
Design, implement, and deploy fully autonomous AI agents that execute workflows across a wide range of functions. Output is production-grade agents capable of performing tasks, making decisions, and interacting with digital environments with minimal human intervention.
Build end-to-end agent systems including input ingestion, reasoning, tool execution, memory, and output. Develop agents that operate across multiple domains and adapt to new use cases without requiring full rebuilds.
Create scalable, modular architectures that enable rapid deployment of agents for different applications. Implement persistent memory systems, decision-making loops, and execution layers that allow agents to function independently while maintaining reliability and control.
Integrate agents with internal and external systems so they can read, write, analyze, and act across platforms. Implement monitoring, logging, and optimization systems to ensure performance, cost control, and accuracy.
Continuously improve agent capabilities by increasing autonomy, reducing failure points, and improving execution speed. Contribute to internal frameworks that standardize how agents are deployed and scaled.
Has previously built and deployed at least one fully functional AI agent end-to-end
Strong experience working with APIs, CLIs, MCPs, and external tool integrations
Deep understanding of LLMs, reasoning patterns, and tool-use orchestration
Ability to connect agents to real systems and enable execution across environments
Proficiency in Python and system-level integrations
Experience with memory systems, retrieval mechanisms, and context handling
Ability to operate in production environments with reliability and performance focus
Preferred
Experience with multi-agent systems and orchestration frameworks
Experience building automation pipelines and execution layers
Familiarity with vector databases and retrieval systems
Experience working with real-world toolchains and system integrations
Ability to translate workflows into autonomous agent behavior
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Build end-to-end agent systems including input ingestion, reasoning, tool execution, memory, and output. Develop agents that operate across multiple domains and adapt to new use cases without requiring full rebuilds.
Create scalable, modular architectures that enable rapid deployment of agents for different applications. Implement persistent memory systems, decision-making loops, and execution layers that allow agents to function independently while maintaining reliability and control.
Integrate agents with internal and external systems so they can read, write, analyze, and act across platforms. Implement monitoring, logging, and optimization systems to ensure performance, cost control, and accuracy.
Continuously improve agent capabilities by increasing autonomy, reducing failure points, and improving execution speed. Contribute to internal frameworks that standardize how agents are deployed and scaled.
Has previously built and deployed at least one fully functional AI agent end-to-end
Strong experience working with APIs, CLIs, MCPs, and external tool integrations
Deep understanding of LLMs, reasoning patterns, and tool-use orchestration
Ability to connect agents to real systems and enable execution across environments
Proficiency in Python and system-level integrations
Experience with memory systems, retrieval mechanisms, and context handling
Ability to operate in production environments with reliability and performance focus
Preferred
Experience with multi-agent systems and orchestration frameworks
Experience building automation pipelines and execution layers
Familiarity with vector databases and retrieval systems
Experience working with real-world toolchains and system integrations
Ability to translate workflows into autonomous agent behavior
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