AI Architect

Job Description

Serve as the principal architect for AI-enabled solutions supporting Finance, designing and delivering secure, scalable, and maintainable systems on AWS. Translate finance business needs into solution architectures that improve the quality, speed, and efficiency of financial planning, reporting, forecasting, and operations. Apply expertise in generative AI, agentic AI, machine learning, data architecture, cloud platforms, and software engineering to address significant business problems. Evaluate emerging technologies and integrate them where they provide measurable value, while ensuring solutions align with enterprise architecture, financial controls, data governance, security, and responsible-AI expectations. Act as a technical leader across Finance and IT, guiding architecture decisions and delivery teams from discovery through production and ongoing improvement.​

Responsibilities:

• ​​Lead architecture and solution design. Translate Finance requirements into end-to-end architectures for generative and agentic AI, machine learning, analytics, data, and software solutions on AWS, including integrations with enterprise platforms and existing finance systems.
• ​Identify and deliver high-value use cases. Partner with Finance stakeholders to assess opportunities such as forecasting, planning, reporting, reconciliation, close-process support, and document or transaction analysis; define solution scope, success measures, and implementation approaches.
• ​Design and implement secure AWS solutions. Select and apply appropriate AWS services and architectural patterns for data ingestion, storage, processing, analytics, AI/ML, application development, and deployment. Address scalability, resilience, performance, cost, and operational support.
• ​Establish data and AI governance. Ensure solutions use trusted, well-managed financial data, with appropriate lineage, quality controls, access management, retention, and auditability. Incorporate human review and escalation where warranted by the use case.
• ​Apply responsible-AI and model-lifecycle practices. Guide model and generative-AI evaluation, validation, monitoring, explainability, versioning, and change management in accordance with applicable enterprise standards and risk controls. Identify limitations and risks, including inaccurate outputs, bias, privacy exposure, and model drift.
• ​Protect financial information and controls. Incorporate security, privacy, segregation of duties, and applicable financial reporting and internal-control requirements into solution designs. Work with Finance, Security, Risk, Compliance, and other control partners to address requirements and support audits.
• ​Guide implementation and technical decisions. Evaluate build, reuse, and purchase options; recommend solutions based on business value, technical fit, risk, and total cost of ownership. Contribute to prototypes or code where needed to validate architecture and resolve key technical challenges.
• ​Advise stakeholders and delivery teams. Provide technical consultation to Finance and IT leaders, product owners, engineers, data teams, and operations partners. Communicate architectural choices, dependencies, risks, and trade-offs clearly to technical and nontechnical audiences.
• ​Promote reliable operations and continuous improvement. Define production-readiness expectations, including testing, deployment, monitoring, incident response, documentation, and ongoing cost and performance optimization. Incorporate user feedback and operational lessons into solution improvements.
• ​Provide technical leadership and uphold standards. Work autonomously with minimal direction, mentor architects and delivery team members as appropriate, and promote adherence to enterprise architecture, software-development, cloud, data, security, conduct, and other applicable corporate policies.

Qualifications

• Bachelor’s Degree with 7 years of relevant experience; Master’s Degree with 6 years of experience; or PhD with 2 years of experience.
• Respective years of experience in application development, solution architecture for AI applications.
• ​In-depth knowledge of the systems development life cycle, solution architecture, and the design and delivery of production-grade cloud, data, and analytics solutions for generative and agentic AI and ML applications.
• ​Demonstrated experience designing or implementing solutions on AWS, including appropriate services and patterns for data management, security, integration, deployment, monitoring, and cost optimization.
• ​Strong understanding of generative and agentic AI capabilities, limitations, and lifecycle practices, including evaluation, validation, monitoring, and responsible use.
• ​Knowledge of data governance, privacy, access control, auditability, and financial controls, with the ability to incorporate applicable requirements into solution designs.

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