About this role
Job title: Principal, AI Engineer
About the Role
As a key member of the AI Engineering team, you will lead the design, development, and deployment of production-grade generative AI systems. You will build secure, scalable AI platforms powered by LLMs on Azure Foundry, collaborating across model providers and business units in a regulated financial environment.
What You'll Do
- Lead development of a portfolio of client-facing AI capabilities and integration methods, embedding them into client front ends and interactive dashboards to ensure these capabilities are well aligned and meet required validation and responsible AI standards.
- Develop and oversee enterprise-wide approach to integrating structured and unstructured data into NT’s enterprise AI framework in collaboration with the AI architecture, AI engineering, and data platform engineering teams.
- Develop and lead the practice of data pipeline engineering, rationalizing custom data integrations over time to establish common methods and approaches.
- Architect and implement data pipelines that integrate structured and unstructured data from internal banking systems, external feeds, and cloud platforms for AI/ML use cases.
- Drive our semantic architecture and engineering approach for Northern Trust intelligence to advance enterprise context engineering and architecture disciplines.
- Collaborate with the AI consulting team, business units, data scientists, model risk teams, and other stakeholders to understand data requirements for AI models supporting key use cases such as portfolio management, quants & research, reconciliations, and customer intelligence; drive engineering specifications and delivery for these capabilities.
- Ensure sound practices are executed to deliver and maintain metadata management, data lineage, and audit trails for AI data assets as development and operations progress.
What We're Looking For
- Experience leading development of client-facing AI capabilities and embedding them into front ends/dashboards, with strong focus on validation and responsible AI in regulated financial environments.
- Experience developing and overseeing data integrations, including structured and unstructured data, into an enterprise AI framework; collaboration with AI architecture and data platform teams.
- Proficiency in data pipeline engineering and standardization of integrations.
- Ability to architect and implement data pipelines that ingest data from internal systems, external feeds, and cloud platforms for AI/ML use cases.
- Experience driving semantic architecture and enterprise context engineering.
- Strong collaboration skills across AI consulting, business units, data scientists, risk management, and other stakeholders to translate data requirements into engineering specifications.
- Familiarity with portfolio management, quantitative research, reconciliations, or customer intelligence use cases in financial services is a plus.