AI Agent Developer

HonorHealth•Virtual - Arizona, AZ

3d•Remote

About The Position

The AI Agent Developer is responsible for designing, building, deploying, and maintaining AI agents and agent-enabled workflows that support enterprise productivity, decision support, and process execution at HonorHealth. This role focuses on converting business use cases into reliable, governed AI systems that can reason over context, interact with approved knowledge sources, call tools or services, and support multi-step work. The AI Agent Developer operates at the application and workflow layer rather than the model research layer. This role partners with business stakeholders, AI Trainers, software engineers, analysts, and governance partners to build agent capabilities that are secure, measurable, maintainable, and aligned with real operational needs. The role is accountable for agent design, integration, testing, reliability, and controlled deployment across approved enterprise use cases.

Requirements

Bachelors in Computer Science or 4 years equivalent experience, Software Engineering, Information Systems, Data Engineering, or related field Required
2 years, Experience with APIs, application integration, cloud-based platforms, or workflow orchestration Required
3 years, Software engineering, systems integration, workflow automation, AI application development, or related experience Required

Nice To Haves

Masters in Computer Science, Artificial Intelligence, Data Science, or related field Preferred
Experience with LLM-enabled applications, prompt design, retrieval patterns, agent frameworks, or tool-calling architectures Preferred
Experience in healthcare, HR, or other regulated enterprise environments Preferred
Experience with Microsoft Copilot Studio and/or Google Cloud Platform (GCP) AI tools Preferred
Relevant cloud, AI platform, or software engineering certifications Preferred

Responsibilities

Agent Design and Development: Design and build AI agents and agent-enabled workflows using approved enterprise platforms, APIs, orchestration tools, and large language model capabilities. Translate business requirements into agent logic, task definitions, prompt structures, decision paths, and interaction patterns. Develop agent behaviors for multi-step reasoning, content retrieval, workflow support, and task execution within approved boundaries. Configure agent inputs, outputs, memory patterns, permissions, and interaction rules based on intended use.
Tool, Data, and System Integration: Connect agents to approved enterprise tools, systems, APIs, knowledge bases, workflow platforms, and data sources. Build and maintain integrations that enable secure and reliable information retrieval or task support. Collaborate with technical teams to ensure connectivity, authentication, data flow, and architecture alignment support intended agent behavior. Help define reusable patterns for agent integration across multiple use cases.
Testing, Evaluation, and Reliability: Develop and execute test plans for AI agent behavior, edge cases, workflow reliability, and failure recovery. Monitor agent outputs, tool calls, workflow consistency, and production behavior to identify issues, drift, or reliability concerns. Debug agent failures, integration errors, prompt breakdowns, or logic conflicts and implement corrective improvements. Support controlled rollout and stabilization of new agent capabilities.
Governance, Security, and Guardrails: Ensure agent configurations operate within approved governance, access, privacy, and security boundaries. Implement controls that support safe use, role-based access, escalation limits, and transparent handling of sensitive information. Partner with governance, privacy, compliance, or security stakeholders when agent workflows intersect with regulated or high-risk use cases. Document intended scope, limitations, approved sources, and operational guardrails for deployed agents.
Documentation and Technical Standards: Create and maintain technical documentation for agent architecture, integration design, configuration logic, deployment steps, and change history. Support coding standards, reproducibility, maintainability, and reusable design patterns across agent implementations. Contribute to technical best practices for AI-enabled workflow development and lifecycle support.
Cross-Functional Collaboration and Improvement: Partner with AI Trainers, software engineers, analysts, product/initiative leads, and subject matter experts to improve deployed agent performance and utility. Translate feedback from users and stakeholders into technical enhancements, configuration changes, or architecture improvements. Support ongoing maturation of enterprise AI-agent capabilities through disciplined iteration and lessons learned.
Performs other duties as assigned