Job Title: Principal, Software Architect
Location: Remote
Duration: 6 months
Pay Rate: $90/hr - $100/hr (DOE) 

Role:
Help accelerate AI transformation across finance and accounting by designing and personally helping deliver complex AI solutions. The Principal AI Solutions Architect partners with the AI Engineering Lead and engineers to turn business problems into secure, tested, maintainable production systems.
This is a hands-on architecture and delivery engagement. The architect contributes production code, builds reference implementations, develops automated tests and AI evaluations, supports deployments, and coaches engineers through implementation. Success is measured by working solutions, reusable engineering assets, and knowledge transferred to the team.

Responsibilities: 

• Partner with business and product stakeholders to understand finance workflows, assess feasibility, and define solution scope, acceptance criteria, and measurable business outcomes.
• Lead architecture for assigned complex AI initiatives, documenting decisions and tradeoffs across agent orchestration, retrieval, enterprise data access, integration, security, cost, and operational support.
• Personally build critical components and reference implementations, including Python services, retrieval pipelines, tool integrations, and agent workflows; carry viable prototypes into production with the team.
• Develop automated tests and representative AI evaluation datasets covering groundedness, retrieval quality, tool execution, business-rule validation, failure handling, and regression prevention.
• Implement appropriate access controls, audit trails, human approvals, and safeguards for sensitive finance data and consequential actions.
• Work with engineers to implement CI/CD, environment configuration, deployment and rollback procedures, monitoring, and operational runbooks.
• Diagnose production issues and improve solution reliability, latency, quality, and operating cost using observed results.
• Coach engineers through pairing, design reviews, code reviews, and progressively transferred implementation ownership.
• Provide technical guidance to vendor delivery teams on assigned solutions and review their implementation against agreed acceptance criteria.
• Leave behind maintainable code, architecture decisions, reusable patterns, evaluation assets, and deployment documentation that the permanent team can own.

• Extensive experience in enterprise software engineering and solution architecture, including recent personal implementation responsibility for production systems.
• Demonstrated delivery of production generative AI solutions using LLMs, retrieval-augmented generation, agent workflows, or intelligent document processing.
• Strong Python engineering skills and practical experience with APIs, enterprise data systems, SQL, automated testing, and maintainable services.
• Experience evaluating AI behavior using representative datasets, measurable acceptance criteria, failure analysis, and regression checks.
• Experience deploying and operating cloud solutions with CI/CD, monitoring, access controls, and support procedures.
• Ability to translate business processes into practical technical designs and explain architecture decisions to engineering and business audiences.
• Demonstrated technical mentoring, collaboration with distributed teams, and knowledge transfer to permanent engineering staff.
• Preferred Qualifications:
• Finance, accounting, ERP, or other enterprise workflow experience, including document-intensive and controlled business processes.
• Hands-on experience with Azure OpenAI, Azure AI Foundry, Copilot Studio, Power Platform, or Dynamics 365 Finance & Operations. The current environment is Microsoft-centered; candidates with other cloud experience should demonstrate a credible path to implementation in this environment.
• Experience with agent orchestration frameworks, vector or hybrid search, structured outputs, tool calling, and human approval workflows.
• Experience guiding vendor engineering teams and collaborating with AI Centers of Excellence, architecture, security, and compliance functions.
• Experience translating platform or transformation strategy into reusable, supported engineering assets.