Software Engineer – AI Systems, Go

Stanbridge University

. Design, develop, test, deploy, and operate production-quality software and backend services, primarily using Go .

Posted 10/23/2026full-timeRemote • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in developing and operating production-quality software and backend services using Go, with a strong focus on AI application architecture and reliable system design. Capable of owning technical problems from investigation through implementation while ensuring observability and performance optimization.

Highest-signal resume keywords

Go (Golang) DevelopmentAI Application ArchitectureDistributed Systems EngineeringHTTP API DesignTesting Discipline for Non-Deterministic Behavior

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Production Software DevelopmentBackend Services DeploymentRelational DatabasesSQLConcurrencyAsynchronous ProcessingError HandlingQuality GatesObservabilityState Management

Soft Skills

Analytical JudgmentProblem-SolvingCollaboration

Tools & Technologies

AI Agent FrameworksContainersCloud InfrastructureCI/CDEvent-Driven Architectures

Industry Keywords

AI AgentsLLM EvaluationHuman-in-the-Loop WorkflowsStructured-Output ValidationData IsolationSecurity Considerations

Tech Stack

Tools & technologies

CloudSQLGo

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

Substantial professional experience developing and operating production software, with strong experience in Go (Golang) or demonstrated depth in another backend language with the ability to become productive in Go quickly
Hands-on experience shipping an LLM-backed application, AI agent, or agent-based system into production and supporting it after deployment
Strong understanding of AI application architecture, including how models, agents, tools, APIs, data sources, services, and application logic interact
Demonstrated distributed-systems engineering knowledge, including concurrency, asynchronous processing, queues, idempotency, retries, partial failure, state management, and recovery
Experience designing systems that remain reliable when individual services, providers, or workflow stages fail
Demonstrated testing discipline for systems involving non-deterministic behavior
Strong experience designing and consuming HTTP APIs
Experience with relational databases, SQL, and persistent application state
Experience building, deploying, monitoring, and troubleshooting backend services in production environments
Understanding of software architecture, testing, debugging, observability, and production engineering practices
Ability to independently own ambiguous technical problems from investigation through production implementation
Strong analytical judgment and ability to balance reliability, quality, performance, complexity, and cost
Applicants must be legally authorized to work in the United States
Must reside in an eligible state at the time of employment
Official transcripts are required prior to hire
Degrees earned outside the United States must be evaluated by a recognized credential evaluation service
Must be able to perform each essential duty satisfactorily and meet stated physical requirements
Preferred: experience with AI agent frameworks, RAG, embeddings, vector databases, semantic search, LLM evaluation and observability, judge models, structured-output validation, human-in-the-loop workflows, media pipelines, event-driven architectures, containers, cloud infrastructure, CI/CD, prompt injection, authorization boundaries, data isolation, and security considerations

Benefits

Comp & perks

Health Care Plan (Medical, Dental & Vision)
Retirement Plan (401k)
Exciting university events
Seasonal motivational health and wellness challenges
Work/Life Balance initiatives
Onsite wellness program / Staff Chiropractor
Life Insurance (Basic, Voluntary & AD&D)
Paid Time Off (Vacation, Sick & Public Holidays)
Family Leave (Maternity, Paternity)
Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions