Partner deeply with the business organization to understand their data, workflows, operator behavior, and where AI can drive measurable improvement in throughput, quality, and customer outcomes.
Architect and lead the development of foundational and agentic AI capabilities for the partner domain—applying modern modeling techniques, retrieval-augmented architectures, and agentic patterns at enterprise scale.
Design long-running and multi-step agents that reason over rich enterprise context to deliver measurable business outcomes within a regulated environment.
Build for production from day one —model evaluation harnesses, drift detection, monitoring, and deterministic deployment patterns that meet the bank's Model Risk Management and AI governance bar.
Operate across the data and modeling application boundary —deploying models into the modeling environment while consuming data and context from the regulated data platform, in close coordination with the platform team.
Influence architecture across two organizations —serving as the senior AI voice in the partner team's technical forums and as the domain voice in Cognitive AI Solutions architectural discussions.
Mentor and uplevel engineers in both organizations on agentic AI, foundational modeling, and applied AI engineering practices.
Translate technical progress into executive narrative —supporting demos and presentations to senior leadership and partner stakeholders.

7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
7+ years of applied machine learning / AI engineering experience, including substantial production deployments at enterprise scale.
Demonstrated experience designing and shipping agentic AI capabilities—multi-step reasoning agents, tool-using agents, long-running agents, or comparable production agentic systems.
Strong systems engineering instincts—comfortable owning end-to-end model development, evaluation, deployment, monitoring, and iteration.
Excellent collaboration, technical communication, and problem-solving skills, with a track record of working across large-scale teams and initiatives to ship real business outcomes.
Prior experience in financial services applications (e.g., banking operations, complaints, wholesale operations) or comparable regulated enterprise domains.
Experience working under Model Risk Management governance for production model deployment.
Background with knowledge graphs, vector retrieval, and retrieval-augmented agentic patterns.
Master's or PhD in a relevant field.
Experience working in a partner-facing engineering engagement model with business organizations.

Principal Applied AI Engineer

Key skills