AI Solution Engineer

About the role

A hybrid builder who finds the highest-leverage problems across WorldStrides, prototypes working solutions with AI-assisted development, and hands them off to engineering for enterprise scale.

Responsibilities

• Embed with stakeholders across lines of business to understand how work actually gets done and where the real opportunity is.
• Identify and frame opportunities, then qualify them: is the problem worth solving — material, real, measurable, and feasible to build?
• Build the case for the work — quantify the prize, whether that's revenue or growth, a better customer experience, or hard savings, cost avoidance, and capacity released — and pressure-test it with finance and product partners before committing the team's time.
• Design and build both to support proof-of-concept and pilot — working software, not slideware.
• Use AI-assisted development to move fast: speed and learning matter more than polish at the POC stage.
• Connect prototypes to real systems of record (i.e., Dataverse, in-house systems) through approved integration patterns, reading and writing only via sanctioned product-owner requests.
• Run pilots with real users, instrument outcomes, and iterate against evidence.
• Hand off — land the plane with IT, not around it. Build to be portable from day one: clean, reviewable code that engineering can adopt rather than rebuild.
• Partner with the IT organization to graduate prototypes to production, and leverage org-wide DevOps and SDLC (trunk-based development, PR and peer review, CI/CD, observability) as those capabilities mature.
• Document architecture, decisions, and assumptions so the work survives the handoff.
• Feed the system. Every project is a live R&D loop. Surface the patterns, edge cases, and insights you find so they shape the broader product and platform roadmap.
• Partner effectively across multiple business areas and levels of leadership to identify, appreciate, and leverage varying perspectives, ways of working, and priorities.

Requirements

• Strong customer focus and insights-driven prioritization.
• You care deeply about delivering solutions that create real customer value, support business goals, and compound the leverage of our technologies and operations.
• 1-3 years building software, products, or automations in a role where you owned outcomes — not just executed assigned tickets. (Strong new-grad candidates with a demonstrable portfolio of or substantive assignments will be considered for the entry level.)
• Demonstrated ability to build full-stack: you can stand up a working front end and wire it to back-end logic and data.
• Evidence you can run discovery with stakeholders or users — you've talked to real people, understood their need or their problem, and translated it into something you built. Say so explicitly, with the outcome.
• AI-assisted development fluency: you build with AI tools (coding agents, LLM application patterns), not around them. We care that you can learn new AI tooling fast and that you’re staying up-to-date on the latest advancements — far more than which specific tools you've used before.
• Clear written and verbal communication. You can explain a tradeoff to a business leader and a data model to an engineer in the same afternoon.
• Bias toward shipping. You'd rather have a rough working prototype in front of a user this week than a perfect plan next month.
• Strongly preferred Experience integrating applications with enterprise systems of record via APIs.
• Familiarity with the Microsoft ecosystem (Azure, Azure DevOps, Dynamics/Dataverse, M365) or the ability to ramp into it quickly.
• Exposure to LLM application development — prompt design, retrieval-augmented patterns, agent/tool-use chains, and especially evaluation (building checks that catch hallucinations and regressions before they reach a pilot).
• Comfort working inside messy, real-world business constraints — legacy workflows, fragmented data, security and compliance gates.

Qualifications

• Technical Front-end: React, modern JS/TypeScript, responsive UI.
• Back-end & data: API design and integration, SQL, relational data modeling; comfort reading/writing to systems of record under governance.
• AI-assisted build: coding agents and AI development tools; LLM application patterns including prompting, RAG, tool/agent orchestration, and eval design.
• Engineering hygiene: version control and trunk-based development, pull requests and peer review, secrets kept out of source control, SSO/AD-based identity.
• Familiarity with CI/CD and observability is a plus.

Pay

Compensation is competitive and commensurate with experience.

Schedule

Flexible work schedule providing on-site, remote, and virtual office opportunities.