Principal AI Solutions Architect

We are seeking a highly experienced, hands-on Principal AI Solutions Architect to help drive AI transformation across finance and accounting operations. This individual will be responsible for designing, developing, and delivering enterprise-grade AI solutions that solve complex business challenges. This is not a purely strategic or advisory architecture role. The ideal candidate will be a strong technical architect who remains actively involved in coding, building AI solutions, testing, deployment, and production support. Working closely with the AI Engineering Lead and engineering teams, this individual will translate business requirements into secure, scalable, and maintainable production solutions while mentoring engineers and establishing reusable development practices., * Lead the architecture, design, and hands-on delivery of complex AI solutions supporting finance, accounting, and enterprise business workflows.

• Partner with business stakeholders, product teams, and engineers to translate business challenges into technical requirements, solution designs, and measurable outcomes.
• Design and develop production-ready generative AI solutions leveraging LLMs, retrieval-augmented generation (RAG), AI agents, and intelligent document processing.
• Personally contribute production code using Python, including API integrations, retrieval pipelines, agent workflows, and reusable technical components.
• Develop automated testing frameworks and AI evaluation processes to measure accuracy, retrieval quality, business-rule compliance, reliability, and performance.
• Implement enterprise security controls, access management, audit logging, approval workflows, and safeguards for sensitive financial information.
• Collaborate with engineering teams on CI/CD pipelines, cloud deployments, monitoring, troubleshooting, and ongoing production support.
• Identify and resolve technical issues related to AI performance, latency, reliability, scalability, and operational costs.
• Provide hands-on technical mentorship through code reviews, design reviews, pairing, and knowledge transfer.
• Guide internal and vendor engineering teams to ensure solutions meet technical standards, business requirements, and acceptance criteria.
• Develop and maintain architecture documentation, reusable code, evaluation assets, deployment procedures, and engineering best practices.

• Extensive experience in enterprise software engineering and solutions architecture, with recent hands-on development and production implementation experience.
• Proven experience designing, building, and deploying production generative AI applications, including LLMs, RAG solutions, agent-based workflows, or intelligent document processing.
• Strong hands-on Python development experience, including API development, enterprise integrations, SQL, automated testing, and maintainable application design.
• Experience developing AI evaluation frameworks, test datasets, performance metrics, and regression testing processes.
• Strong understanding of cloud architecture, CI/CD pipelines, monitoring, security, and production deployment practices.
• Experience integrating AI solutions with enterprise applications, databases, and business systems.
• Ability to translate complex business requirements into practical technical solutions and communicate architectural decisions to technical and nontechnical stakeholders.
• Demonstrated experience mentoring engineers, reviewing code, collaborating with distributed teams, and transferring technical ownership., * Experience supporting finance, accounting, ERP, or enterprise business transformation initiatives, particularly document-intensive or compliance-driven workflows.

• Hands-on experience with Microsoft technologies, including: o Azure OpenAI o Azure AI Foundry o Microsoft Copilot Studio o Power Platform o Dynamics 365 Finance & Operations

• Experience with alternative cloud platforms is acceptable, provided the candidate can demonstrate the ability to implement solutions within a Microsoft-centered environment.
• Experience with AI agent orchestration frameworks, vector databases, hybrid search, structured outputs, tool calling, and human-in-the-loop approval processes.
• Experience collaborating with AI Centers of Excellence, enterprise architecture, information security, compliance, and vendor engineering teams.
• Experience establishing reusable AI engineering frameworks, reference architectures, and production development standards.