Role: Principal AI Solutions Architect
Location: Remote
Work Schedule: 8:00 AM – 5:00 PM EST (1-hour lunch)
Hours: 40 hours per week; no overtime or weekend work
Duration: 6-month contract
Extension/Conversion: Moderate possibility of extension or conversion, depending on performance and business requirements
Interview Process: Two rounds via Microsoft Teams
About the Role
We are seeking a Principal AI Solutions Architect to accelerate AI transformation across finance and accounting by designing and delivering complex, enterprise-grade AI solutions.
In this hands-on role, you will partner with the AI Engineering Lead and engineering teams to translate business challenges into secure, tested, scalable, and maintainable production systems.
This is an architecture and implementation engagement requiring active participation in production coding, reference implementations, automated testing, AI evaluations, deployment support, and technical mentoring. Success will be measured by the delivery of working solutions, reusable engineering assets, and effective knowledge transfer to the permanent engineering team.
Key Responsibilities
- Business Analysis and Solution Definition: Partner with business and product stakeholders to understand finance workflows, assess technical feasibility, define solution scope, establish acceptance criteria, and identify measurable business outcomes.
- AI Solution Architecture: Lead architecture for complex AI initiatives, documenting key decisions and trade-offs involving agent orchestration, retrieval, enterprise data access, system integration, security, cost, and operational support.
- Hands-On Engineering and Implementation: Personally develop critical components and reference implementations, including Python services, retrieval pipelines, tool integrations, and agent workflows. Help move viable prototypes into production in collaboration with the engineering team.
- AI Testing and Evaluation: Develop automated tests and representative AI evaluation datasets to assess groundedness, retrieval quality, tool execution, business-rule validation, failure handling, and regression prevention.
- Security and Governance: Implement appropriate access controls, audit trails, human approval mechanisms, and safeguards for sensitive financial data and consequential actions.
- Deployment and DevOps: Collaborate with engineers to implement CI/CD pipelines, environment configuration, deployment and rollback procedures, monitoring, and operational runbooks.
- Production Support and Optimization: Diagnose production issues and continuously improve solution reliability, response latency, output quality, and operating costs using observed performance data.
- Technical Mentoring: Coach engineers through pair programming, architecture and design reviews, code reviews, and progressive transfer of implementation ownership.
- Vendor Coordination: Provide technical direction to vendor delivery teams and evaluate their implementations against agreed technical requirements and acceptance criteria.
- Knowledge Transfer and Documentation: Deliver maintainable code, architecture decision records, reusable engineering patterns, evaluation assets, and deployment documentation that the permanent team can independently maintain and support.
Required Qualifications
- Extensive experience in enterprise software engineering and solution architecture, including recent hands-on responsibility for implementing production systems.
- Demonstrated experience delivering production-grade generative AI solutions using large language models (LLMs), retrieval-augmented generation (RAG), agent workflows, or intelligent document processing.
- Strong Python development skills and practical experience with APIs, enterprise data systems, SQL, automated testing, and maintainable software services.
- Experience evaluating AI solutions using representative datasets, measurable acceptance criteria, failure analysis, and regression testing.
- Hands-on experience deploying and operating cloud-based solutions, including CI/CD, monitoring, access controls, and production support procedures.
- Ability to translate business processes and requirements into practical technical architectures and communicate design decisions effectively to both technical and business stakeholders.
- Demonstrated experience mentoring engineers, collaborating with distributed teams, and transferring technical knowledge to permanent engineering staff.
Preferred Qualifications
- Experience in finance, accounting, ERP, or other enterprise business workflows, particularly document-intensive and highly controlled processes.
- Hands-on experience with Microsoft AI and enterprise platforms, including Azure OpenAI, Azure AI Foundry, Copilot Studio, Power Platform, or Dynamics 365 Finance & Operations.
- Candidates with experience in other cloud ecosystems should demonstrate a credible approach to implementing solutions within the current Microsoft-centered environment.
- Experience with agent orchestration frameworks, vector search or hybrid search, structured outputs, tool calling, and human-in-the-loop approval workflows.
- Experience guiding vendor engineering teams and collaborating with AI Centers of Excellence, enterprise architecture, security, and compliance functions.
- Experience translating AI platform or transformation strategies into reusable, supportable engineering assets.
Work Environment and Expectations
- Fully remote engagement.
- Standard working hours: 8:00 AM–5:00 PM EST, including a one-hour lunch break.
- 40 hours per week, with no overtime or weekend work.
- Initial 6-month contract, with a moderate possibility of extension or conversion based on performance and business requirements.
- The position requires a hands-on technical architect who can design, code, test, deploy, troubleshoot, and mentor—not solely provide high-level architectural guidance.
