Enterprise AI Architect

SNHU Careers

Enterprise AI Architect leading AI capabilities across Southern New Hampshire University's digital ecosystem. Responsible for aligning AI solutions with enterprise strategies and architecture standards.

Posted 7/8/2026full-timeRemote • Alabama, Arizona, Florida, Hawaii, Idaho, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Mississippi, Missouri, Montana, New Hampshire, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, West Virginia, Wisconsin, Wyoming • 🇺🇸 United StatesSeniorLead💰 $137,839 - $220,582 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in defining and evolving enterprise AI architecture, integrating AI solutions with enterprise platforms, and implementing governance practices for responsible AI usage. Proficient in AI/ML solution design, lifecycle management, and collaboration with cross-functional teams to drive operational efficiency and student success.

Highest-signal resume keywords

AI Architecture DesignAI/ML Solution DeliveryEnterprise IntegrationMLOps PracticesCloud Platform Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI/ML FrameworksEnterprise Architecture FrameworksData Pipeline DesignAPIsMicroservicesSystem IntegrationModel MonitoringRisk MonitoringAutomationAnalytics

Soft Skills

MentoringCollaborationFacilitation

Tools & Technologies

TensorFlowPyTorchAzureAWSGCPSalesforceServiceNowWorkdayD2LBanner

Industry Keywords

FERPAHIPAAGDPRAgile DeliveryTOGAFZachman

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformMicroservicesPyTorchServiceNowTensorflow

About the role

Key responsibilities & impact

Define and evolve enterprise AI architecture, standards, reference models and implementation guidelines to support consistent solution delivery across business functions
Align AI initiatives to enterprise strategies, roadmaps, and business priorities
Identify and prioritize AI opportunities that drive student success and operational efficiency
Design scalable AI platforms and solutions across data, application, and technology domains
Provide guidance on how AI, ML, and automation will improve current and future-state architectures
Lead design and implementation of AI-powered solutions (e.g., LLMs, agents, automation, and analytics)
Integrate AI capabilities with enterprise applications and data platforms
Guide evaluation and selection of AI/ML models, frameworks, and vendors
Establish reusable patterns, APIs, and integration approaches
Establish governance practices covering model oversight, responsible AI usage, security controls, compliance expectations, and lifecycle management
Establish AI governance, including responsible AI, security, privacy, and compliance (FERPA, HIPAA, GDPR)
Establish lifecycle management practices (MLOps/LLMOps, model monitoring, drift detection, retraining)
Implement performance, bias, and risk monitoring frameworks
Ensure secure AI architecture (e.g., data protection, isolation, adversarial defense)
Partner with business, product, data, and engineering teams to provide AI solutions
Provide architectural guidance to architects and delivery teams
Facilitate workshops, POCs, demos and AI adoption projects identifying viable opportunities where AI can be a positive differentiator
Mentor teams on AI best practices, patterns, and frameworks

Requirements

What you’ll need

15+ years in information Technology
8+ years in enterprise or solution architecture
3+ years in AI/ML solution design and delivery
Experience integrating AI solutions with enterprise platforms (e.g., Salesforce, ServiceNow, Workday, D2L, Banner)
Experience with enterprise architecture frameworks (e.g., TOGAF, Zachman) and Agile delivery
Experience with AI/ML frameworks (e.g., TensorFlow, PyTorch), LLMs, and agent-based architectures (e.g., RAG, AutoGPT, LangChain)
Experience with Cloud platforms (Azure, AWS, GCP) and data/AI pipeline design
Experience with APIs, microservices, system integration, and enterprise data management
Experience with ML Ops/LLM Ops practices including CI/CD, monitoring, and deployment patterns across (Azure, AWS, GCP)
Experience with enterprise standards for principles, patterns and guidelines on the use of AI within the SNHU environment

Benefits

Comp & perks

High-quality, low-deductible medical insurance
Low to no-cost dental and vision plans
5 weeks of paid time off (plus almost a dozen paid holidays)
Employer-funded retirement
Free tuition program
Parental leave
Mental health and wellbeing resources