• Evaluate technical feasibility of AI-enabled business solutions and identify architecture considerations impacting implementation viability.
• Evaluate architectural patterns supporting AI-enabled workflows
• Assess feasibility of AI approaches using enterprise data and integration patterns
• Identify technical dependencies impacting implementation sequencing
• Provide guidance on tradeoffs between approaches
• Identify reusable components that may support multiple enterprise use cases
• Document technical constraints that may impact delivery timelines
• Provide guidance, evaluations and solutions for applied AI engineering patterns relevant to enterprise use cases

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or related technical field
• 10+ years of experience in software engineering, AI engineering, machine learning engineering, or solution architecture roles.
• 1-2 years experience delivering retrieval augmented generation (RAG), embeddings pipelines, semantic search, or document intelligence workflows preferred
• Experience working in financial services, fintech, payments, or regulated environments is a plus
• Experience designing or evaluating AI-enabled solution architectures
• Experience working with machine learning, NLP, or large language model applications
• Experience integrating AI capabilities using APIs or cloud-based AI platforms
• Experience evaluating feasibility of AI-enabled workflows using enterprise data sources
• Experience working with cloud environments such as Azure, AWS, or GCP
• Experience collaborating with engineering teams on AI-enabled product features
• Understanding of model reliability, evaluation considerations, and performance tradeoffs
• Ability to translate technical constraints into business implications
• Experience supporting proof-of-concept or prototype efforts is preferred
• Experience working in financial services, fintech, payments, or regulated environments is a plus