Stanbridge University

AI Engineer

• Design, develop, test, deploy, and operate production-quality software and backend services, primarily using Go
• Architect and build AI agents and agent-based systems using tools, APIs, state, and context for complex multi-step workflows
• Design durable workflows that checkpoint, resume, retry, recover, and continue safely after failures or interruptions
• Determine responsibilities across models, agents, tools, APIs, data sources, services, and deterministic application logic
• Design integrations across multiple AI model providers, including routing, failover, rate-limit management, health monitoring, and degradation strategies
• Build safeguards including structured-output validation, error handling, retries, quality gates, evidence validation, and automated recovery
• Develop testing strategies for non-deterministic AI behavior using fixtures, recorded/replayed interactions, regression suites, evaluation harnesses, and quality baselines
• Develop observability through tracing, metrics, logging, evaluation data, and replayable execution histories
• Design human-in-the-loop workflows incorporating review, approval, intervention, and workflow-state modification
• Build and maintain APIs, backend services, relational data models, job-processing infrastructure, and supporting application components
• Develop secure methods for ingesting and processing user-supplied documents and external content
• Optimize reliability, latency, throughput, scalability, output quality, and cost per execution
• Build reusable patterns and shared components for agents, model providers, tools, workflows, and output types
• Own technical problems from investigation and architecture through implementation, deployment, monitoring, troubleshooting, and production operation
• Collaborate with product stakeholders and domain experts to translate requirements into measurable behavior and technical solutions
• Contribute to architectural decisions and engineering standards for AI-powered applications across the University
• Build reliable production AI workflows, improve measurability and quality, reduce latency and cost, and establish foundational AI engineering practices

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Software Engineer – AI Systems, Go