How will this role impact First Command?
The Principal AI Architect will lead the design and implementation of enterprise AI architecture, technical frameworks, and governance standards to ensure responsible, scalable, and strategically aligned adoption of AI technologies. This role focuses on embedding AI best practices into First Command’s enterprise architecture, collaborating closely with data, technology, and governance teams.
What will the employee do in this role?
- Strategic Leadership
- Contribute to enterprise AI strategy by defining architecture principles, technical standards, and governance models.
- Advise senior architects and technology leaders on scalable AI design patterns and integration approaches.
- AI Architecture and Infrastructure
- Define and evolve the enterprise AI architecture blueprint in alignment with First Command’s business strategy, cloud infrastructure, and enterprise architecture frameworks (e.g., TOGAF, BIZBOK).
- Standardize AI platforms, tools, and development practices to support reusability, security, observability, and performance at scale.
- Collaborate with Enterprise Architecture, Cloud Engineering, and IT Operations to integrate LLM endpoints, AI services, and model pipelines into cloud-native environments (e.g., Azure OpenAI, Azure AI Foundry).
- Establish enterprise-wide AI infrastructure patterns, MLOps standards, and orchestration practices that enable lifecycle management and compliance with internal controls.
- Proof-of-Concept (PoC) Management
- Support the design and technical evaluation of AI PoCs to validate feasibility and architectural fit.
- Recommend reusable components and scalable patterns based on PoC outcomes.
- Drive end-to-end PoC delivery—defining objectives, coordinating cross-functional teams, and ensuring validation metrics are aligned with enterprise KPIs.
- Maintain a transparent PoC governance framework that ensures stakeholder visibility, controls experimentation risk, and supports enterprise adoption decisions.
- Cross-Functional Collaboration
- Collaborate with product, engineering, and data teams to embed AI into platforms using approved architecture patterns.
- Partner with governance and compliance teams to ensure AI solutions meet internal standards and regulatory requirements.
- Governance and Risk Management
- Establish and maintain AI governance frameworks aligned with enterprise architecture and Responsible AI principles.
- Define technical policies for model lifecycle management, performance monitoring, and auditability.
- Collaborate with Risk, Legal, Compliance, and Security teams to evaluate AI-related risks, including data privacy, bias mitigation, and third-party model usage.
- Culture and Enablement
- Promote AI best practices and technical literacy across architecture and engineering teams.
- Contribute to internal knowledge hubs and communities of practice focused on scalable AI design.
- External Engagement
- Stay current with industry standards and emerging technologies to inform architectural decisions.
- Engage with external forums and vendors to benchmark practices and explore new tools.
- Supervisory Responsibility
- Provide technical mentorship to AI engineers and data scientists.
- Influence architectural decisions and promote adherence to governance standards.
What Skills & Qualifications do you need?
Required
- Master’s degree in data science, machine learning, AI, or a related field.
- 5-7 years of experience in AI/ML architecture, infrastructure, or engineering roles.
- Proven success designing and implementing enterprise-grade AI solutions aligned with business strategy.
- Deep understanding of AI/ML technologies — including Generative AI, LLM lifecycle management, and Responsible AI frameworks — and their integration into enterprise architecture.
- Hands-on familiarity with modern AI orchestration tools (e.g., LangChain, RAG pipelines, agent frameworks, knowledge graphs).
- Strong ability to influence architectural decisions and drive governance across the AI delivery lifecycle.
Preferred
- Experience in financial services or other regulated industries.
- Familiarity with architecture and capability frameworks (e.g., TOGAF, BIZBOK, LeanIX).
- Experience with cloud-native AI platforms such as Azure OpenAI, Azure AI Foundry, Microsoft Copilot, or similar.
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