AI Solutions Architect

Position Description

PAR Technology's AI Enablement & Transformation team delivers governed AI solutions that improve how internal business teams work across HR, Finance, Legal, Operations, Customer Success, Marketing, Sales and Support.

This is a hands-on AI solution architecture and delivery role. The person in this role is expected to build, not only recommend. They will turn ambiguous business needs into practical AI solutions, determine the right technical approach, create and launch solutions using PAR-approved AI platforms, and remain accountable for their performance after release. The role will partner closely with IT and Enterprise Systems, with solutions moving into formal production pathways as PAR's enterprise AI operating model matures.

This is not a data analyst, reporting or traditional SaaS product-development role. It is also not primarily an integration or MCP development role. We are looking for an AI solutions architect who can determine what should be built, select the right approach, create the solution and remain accountable for whether it works reliably after launch.

The role will also support PAR's AI economics and token optimization work by evaluating model choice, usage patterns, solution design, cost and business value across PAR's AI tooling investments.

What We're Looking For
• Demonstrated experience designing and delivering AI-powered solutions, agents, automations or internal tools used by real business teams.
• Strong solution architecture judgment: the ability to move from an unclear business problem to a practical technical design, delivery plan and working solution.
• Hands-on experience with enterprise AI platforms such as Claude Enterprise, Microsoft Copilot, Copilot Studio, Azure AI or comparable tools.
• Ability to build and configure AI solutions using system instructions, knowledge sources, tool use, workflow logic, structured outputs and appropriate human review.
• Ability to evaluate whether a need is best solved through platform configuration, an agent, workflow automation, a lightweight application, an integration or no build at all.
• Experience taking prototypes beyond demonstration quality, including testing, permissions, documentation, monitoring, support planning and iterative improvement.
• Working knowledge of APIs, MCP, webhooks and enterprise integration patterns sufficient to evaluate and guide their use; deep integration engineering is not the primary focus of this role.
• Familiarity with Microsoft 365, SharePoint and Azure environments.
• Ability to translate technical decisions, risks and tradeoffs for non-technical business leaders.
• Strong prioritization and delivery management skills, with the ability to own an intake queue without becoming only an intake coordinator.

Preferred Experience
• Experience delivering internal AI solutions across multiple corporate functions in a SaaS or complex enterprise environment.
• Experience partnering with IT, Enterprise Systems, Security, Legal or enterprise architecture teams.
• Experience using AI-assisted development tools to accelerate solution delivery while maintaining standards for quality, security and maintainability.
• Familiarity with AI governance, data classification, access controls and audit requirements.
• Experience measuring adoption, productivity impact, solution quality and cost after launch.

Candidates should be prepared to walk through two AI solutions they personally designed and delivered. We will look specifically at the business problem, how they chose the solution approach, what they personally built or configured, how they tested it, what happened after launch, and what they changed when it did not perform as expected.

What You Will Be Doing and Owning
AI Solution Architecture & Delivery
• Translate business problems, workflows, constraints and desired outcomes into clear AI solution designs.
• Recommend the right technical path for each request, including when PAR should configure, automate, build, buy, integrate or decline a solution.
• Create working internal AI solutions and take them from prototype through governed release and ongoing improvement using PAR-approved platforms and tools.
• Define solution requirements, architecture, dependencies, risks, delivery estimates and success measures.
• Make practical tradeoffs across user experience, quality, speed, cost, maintainability and risk.
• Build reusable solution patterns, components, Skills, agents, prompts and knowledge structures that can be applied across teams.

Post-Production Ownership
• Own AI solutions after launch from the AI Enablement side, including solution quality, reliability, user feedback, adoption, cost and continuous improvement. Partner with IT and Enterprise Systems as solutions transition into PAR's formal production and support processes.
• Establish appropriate evaluation, monitoring, issue management and human-review processes for each solution.
• Troubleshoot solution behavior and coordinate resolution across AI platforms, configurations, knowledge sources and enterprise dependencies.
• Maintain solution documentation, release notes, support guidance and ownership records.
• Retire, redesign or consolidate solutions that do not deliver sufficient value or cannot be supported responsibly.

IT & Enterprise Systems Partnership
• Partner with IT, Enterprise Systems and Security on identity, permissions, data access, environment, supportability and production-readiness requirements.
• Translate business use cases into technical designs that enterprise teams can review and support.
• Create solutions that can transition into PAR's formal production processes as those pathways and standards mature.
• Coordinate technical reviews, resolve blockers and ensure long-term ownership is clear before broader deployment.
• Identify when integrations are required and partner with the appropriate technical teams or specialized builders when deeper integration work is needed.
AI Intake & Portfolio Management
• Review and triage incoming AI solution requests based on business value, feasibility, reuse potential, risk, effort and ongoing support cost.
• Clarify the underlying workflow before recommending a tool or technical approach.
• Maintain a prioritized delivery roadmap and communicate scope, decisions, status and dependencies to stakeholders and the VP of AI Enablement.
• Prevent one-off experimentation from becoming unsupported production tooling.

AI Economics & Platform Evaluation
• Evaluate AI workloads across cost, quality, latency, usage and business value.
• Recommend model, platform or architecture changes that lower cost without weakening the required outcome.
• Validate new models and feature releases against PAR use cases before recommending broader adoption.
• Support leadership reporting with clear technical status, delivery forecasts, usage patterns and cost-optimization opportunities.

Governance & Quality
• Ensure AI solutions align with PAR standards for data handling, access, permissions, documentation and auditability.
• Participate in AI policy and standards development with IT, Security, Legal, Internal Audit and AI Enablement leadership.
• Define practical quality gates for prototypes, limited releases and broader deployment.
• Help business teams understand what responsible ownership looks like after an AI solution launches.

What Success Looks Like
• Business teams receive working AI solutions, not only recommendations or prototypes.
• AI requests move through a clear path from discovery and architecture to launch, measurement and ongoing ownership.
• PAR has reusable solution patterns rather than a growing collection of disconnected one-off tools.
• Launched solutions remain reliable, governed, supportable and measurably useful after release.
• IT and Enterprise Systems have the technical clarity and documentation needed to support future production scale.
• AI platform and token costs are managed through better architecture and model choices, not by unnecessarily limiting valuable use cases.
• The majority of the role's time results in delivered and improved AI solutions, not intake administration, reporting or technical advisory alone.

Interview Process
• Interview 1: Video screen with Talent Acquisition.
• Interview 2: Video interview with the hiring manager via Microsoft Teams.
• Interview 3: Technical solution review and working-session interview with the team via Microsoft Teams. Candidates will discuss a solution they personally designed and delivered and work through a PAR-style internal AI use case.