AI Business Architect - REMOTE

📋 Description

• • Evaluate technical feasibility of AI-enabled business solutions and identify architecture considerations impacting implementation viability.
• • Assess architectural patterns supporting AI-enabled workflows across enterprise systems.
• • Analyze feasibility of AI approaches using existing enterprise data sources and integration patterns.
• • Identify technical dependencies that impact sequencing and timing of AI solution delivery.
• • Provide guidance on tradeoffs between competing AI engineering approaches and implementation strategies.
• • Surface reusable AI components and capabilities that can support multiple business teams and use cases.
• • Document technical constraints and risks that may affect delivery timelines and operational scalability.
• • Advise on applied AI engineering patterns relevant to enterprise use cases including retrieval augmented generation (RAG), embeddings pipelines, semantic search, and document intelligence.
• • Collaborate with the Manager, AI Business Engagement and AI Business Analyst to ensure solution approaches align with enterprise technology standards.
• • Contribute to technical validation, prototyping guidance, and vendor capability evaluations for AI initiatives.
• • Surface architectural, risk, and governance considerations early in the AI lifecycle to support informed enterprise decision-making.
• • Work within a fractional capacity model, potentially as an internal shared resource, consulting partner, or contract engineering expert.
• • Translate technical constraints and architectural decisions into clear business implications for non-technical stakeholders.
• • Support proof-of-concept and prototype efforts for emerging AI capabilities without owning full product delivery.
• • Engage with cloud-based AI platforms and APIs to evaluate integration options for enterprise AI solutions.
• • Ensure alignment of AI architectures with enterprise data governance, security, and compliance requirements.
• • Maintain awareness of model reliability, evaluation metrics, and performance tradeoffs in production AI systems.
• • Work closely with engineering teams to validate feasibility of AI-enabled product features and capabilities.
• • Apply knowledge of machine learning, NLP, and large language model applications to real-world financial services use cases.