Applied AI Engineer

Overview

Position Summary

Reporting to the Chief Information Officer, the Applied AI Engineer will play a pivotal role in developing AI applications for use by the Columbia University Irving Medical Center (CUIMC) community, functioning in a fast-paced and agile way focusing on developing extremely innovative solutions to some of the most challenging problems of the organization.

This role will focus primarily on supporting our administrative, research, clinical and educational missions. This position involves developing applications and systems using AI technologies including applications of leveraging LLM APIs, automation/RPA, and machine learning algorithms to drive efficiencies and new capabilities across various University functions. The successful candidate will collaborate with cross-functional teams to design, build, and maintain advanced AI models and applications.

The ideal candidate is a hands-on technologist with proven experience developing and deploying AI/ML solutions in real-world environments. They are intellectually curious and proactive—equally adept at responding to strategic requests and independently exploring emerging technologies to build foundational knowledge. Exceptional communication skills are essential, as the role requires translating complex technical concepts into clear, actionable insights for both technical and non-technical audiences through materials such as executive briefings, white papers, and live demos.

They are also a self-directed project manager, capable of leading research investigations and AI initiatives from concept through execution with minimal oversight. Above all, the successful candidate is energized by solving difficult problems and applying innovative, responsible approaches to advance CUIMC’s mission in healthcare, education, and research.

Essential Duties:

AI Development and Implementation

• Design, build, and maintain applications using retrieval-augmented generations (RAG) techniques to enhance knowledge retrieval, automate workflows, and support decision-making across CUIMC operations.
• Develop scalable AI tools that can be integrated into CUIMC’s administrative and educational systems.
• Translate stakeholder goals into actionable AI use cases and recommend suitable tools or frameworks.
• Evaluate tradeoffs between model performance, interpretability, and operational scalability when selecting AI approaches.
• Serve as a technical advisor on AI feasibility, integration strategies, and ethical considerations.

Research and Innovation

• Stay current on AI advancements, frameworks, and tooling, with a focus on their applicability in higher education and healthcare.
• Rapidly prototype solutions using open-source technologies; present findings and demos to stakeholders and leadership.
• Participate in exploratory investigations and contribute to long-term strategic roadmaps for emerging technologies.
• Contribute to the development of AI governance strategies, including documentation of model assumptions, limitations, and potential risks.

Technical Support and Documentation

• Troubleshoot and optimize AI applications; provide technical support for production issues as needed.
• Develop and maintain comprehensive documentation for all projects, and prepare reports and presentations to communicate findings to both technical and non-technical stakeholders.

Responsible AI and Institutional Alignment

• Ensure AI applications are designed and deployed with attention to privacy, transparency, and equitable impact.
• Adhere to CUIMC’s data governance, compliance, and institutional review protocols.
• Support development of internal best practices for safe and ethical use of generative and predictive AI models.

Data Analysis

• Perform exploratory data analysis (EDA) to inform AI model development and application logic.
• Ensure data quality, compliance, and integrity in all phases of the AI project lifecycle.
• Define and track success metrics for deployed models: monitor model drift, fairness, and performance over time.

People

• Partner with internal teams (engineers, analysts, data scientists, and stakeholders) to scope needs, develop solutions, and ensure successful implementation.
• Parti…