Enterprise AI Architect

Southern New Hampshire University

1d•$137,839 - $220,582•Remote

About The Position

Southern New Hampshire University (SNHU) is seeking an Enterprise AI Architect to serve as the technical and strategic lead for AI capabilities across the university's digital ecosystem. This role ensures AI solutions align with enterprise strategy, architecture standards, and business priorities while delivering secure, scalable, and ethical outcomes. The architect will partner across business, IT, and data domains to define and implement AI-enabled capabilities, including generative AI, machine learning, automation, and advanced analytics, integrated into core enterprise platforms. This position will also bridge the gap between Customer Success, Product, and Engineering teams, defining how AI transforms collaboration at scale across SNHU's broader tech ecosystem. The role reports to the Vice President of Enterprise Architecture and offers 100% remote work from approved states.

Requirements

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.
Inclusive of alignment with Responsible AI standards and Sustainability goals.

Responsibilities

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.