Director of AI Solutions Partners

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. #LI-Remote

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