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Southern New Hampshire University is a team of innovators. World changers. Individuals who believe in progress with purpose. Since 1932, our people-centered strategy has defined us — and helped us grow a team that now serves over 180,000 learners worldwide.
Our mission to transform lives is made possible by talented people who bring diverse industry experience, backgrounds and skills to the university. And today, we're ready to expand our reach. All we need is you.
Make an impact — from near or far
At SNHU, you'll have the option to work remotely in the following states: Alabama, Arizona, Arkansas, Delaware, Florida, Georgia, Hawaii, Idaho, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Mississippi, Missouri, Nebraska, New Hampshire, New Mexico, North Carolina, North Dakota, Ohio, Oklahoma, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, West Virginia, Wisconsin and Wyoming.
We ask that our remote employees have access to a reliable internet connection and a dedicated, properly equipped workspace that is free of distractions. Employees must reside in, and work from, one of the above approved states.
The opportunity
The Director of AI Solutions Partners will report directly to the Assistant Vice President of AI Engineering and will lead the discovery and solution definition function for SNHU's AI engineering capability. Reporting to the AVP, AI Engineering, the Director will own how SNHU identifies its highest-leverage AI problems and opportunities and turns them into production-grade solutions. The team collaborates with partner teams across the university, carrying a promising idea from the first conversation through to a clearly stated problem, a validated case for building, and a proof-of-concept level feature set an AI Product Pod can deliver.
This is a player-coach leadership role with people-management responsibility. The Director will build and lead the AI Solutions Partner function, the discovery and solution-shaping role in every pod, and work alongside engineering and delivery leadership within AI Engineering so that definition and delivery stay connected. This position will also lead pods directly, particularly the newest and most demanding ones, keeping their own practice current with the standard set for the team.
AI Product Pods are how SNHU builds with AI: small, forward-deployed teams that embed with a partner team, build alongside them on their real problems, and leave them measurably more AI-capable than they found them. A single pod drives one initiative, and the number of active pods is growing. The Director's accountability spans the discovery pipeline that feeds every pod and the practice that keeps problem definition rigorous as the portfolio grows.
The role involves:
Discovery and solution definition: Own the path from a promising opportunity to a clearly defined problem, a validated case for building, and a solution shape a pod can deliver, and sequences that pipeline against available pod capacity.
Building the discipline: Hire, develop, and lead the AI Solutions Partners who do this work inside pods, and set the standard for discovery, problem definition, requirements, and prototyping across the AI organization.
Partnership across SNHU: Work with AI engineering and delivery leadership, Domain Product Owners, AI Operations and Governance, AI Portfolio Operations, and ITS to advance a build culture in which the teams closest to a problem can specify and prototype solutions themselves.
You will work 100% remotely.
What You'll Do:
Discovery and solution definition:
Own the initial stages of the pod lifecycle: opportunity identification, discovery interviews, workflow analysis, and opportunity assessment, through to a problem definition and solution shape an engineering team can act on. Go looking for SNHU's highest-leverage problems and opportunities rather than waiting for them to arrive.
Define the standard for a complete problem definition: the problem, the outcome it serves, the evidence behind both, the data and access the solution depends on, a proof-of-concept level feature set with acceptance criteria, and what scaling would involve if the proof of concept succeeds.
Run intake and triage with AI Engineering leadership and AI Portfolio Operations: what enters the pipeline, what receives a pod, what is deferred, what is discontinued, and in what order against available capacity.
Own the opportunity portfolio and support its prioritization with evidence when competing demands exceed capacity.
Advise clearly when AI is not the right tool, when an existing system or team already addresses the problem, or when a process change would serve better than a build, so that pods stay focused on work that genuinely requires AI.
Partner with AI engineering leadership to define outcomes and evaluation criteria for each initiative, so that delivered work is measured against what it was intended to achieve.
Leading the AI Solutions Partner discipline:
Hire, develop, and lead a team of AI Solutions Partners; own their performance, growth, and career development.
Set the standard for discovery, problem definition, requirements, prototyping, and user validation, at a level of quality that allows pods to hand off cleanly and later teams to reuse what was learned.
Build shared practice assets: discovery playbooks, interview and workflow-mapping guides, opportunity assessment and triage templates, requirements patterns, and practice documentation that lift consistency across the function.
Ensure what pods learn reaches the shared AI Engineering platform and practice rather than staying with the partner who learned it, so that each pod starts from more than the last one did.
Allocate AI Solutions Partners across pods and initiatives, balancing continuity for host teams against the concurrent load any one partner can carry well.
Build durable internal capability: a function that is staffed, capable, and not dependent on any one individual, so that capacity grows with the portfolio.
Mentor across the AI organization and the wider institution on problem definition, discovery, and outcome ownership.
Leading the work directly:
Personally lead discovery and solution definition on the most complex and highest-stakes initiatives, including the first pod in any new domain.
Build prototypes, agent workflows, and working demonstrations that make an idea tangible quickly and reduce risk before a full build is funded.
Stay hands-on with current AI tooling and agentic frameworks and contribute technically rather than only oversee engage with code, reason about architecture, and shape agent and solution design with the engineers who own the build, informing the feasibility, model, cost, and latency trade-offs that determine what gets built.
Pod operating model and collaboration
Own the definition side of the pod model: what gets built and why, with engineering owning how it is built and operated, and joint accountability to the AVP, AI Engineering for whether the pod model delivers.
Work with Domain Product Owners, Business Sponsors, and host-team leadership on what to build and how it fits the operational reality of the teams who own the problems.
Ensure pods operate effectively alongside the host team, whatever its shape: account for the teams, platforms, and solutions already in place, and design for the host team to sustain what ships after the pod concludes.
Contribute to checkpoint reviews across the pod lifecycle, ensuring that leadership decisions are informed, evidenced, and documented.
Surface definition and scope risk early and make scoping and sequencing decisions promptly.
Pod operating model and collaboration
Own the definition side of the pod model: what gets built and why, with engineering owning how it is built and operated, and joint accountability to the AVP, AI Engineering for whether the pod model delivers.
Work with Domain Produc