AI/Machine Learning Engineer

Position Details Position Information

About Fordham Founded in 1841, Fordham is the Jesuit University of New York, offering an exceptional education distinguished by the Jesuit tradition to more than 16,000 students in its nine colleges and schools. It has residential campuses in the Bronx and Manhattan, a campus in West Harrison, N.Y., the Louis Calder Center Biological Field Station in Armonk, N.Y., and the London Centre in the United Kingdom.

The University offers a comprehensive benefits package that includes medical, dental, and vision insurance; flexible spending accounts; retirement plans; life insurance; short and long-term disability; employee assistance program ( EAP ); tuition remission; and generous time off.

Successful candidates should have a knowledge of and commitment to the goals of Jesuit Education.

Title of Position AI/Machine Learning Engineer

Position Number A11100

FLSA Exempt

Scheduled Hours Per Week 35

Department Enterprise AI

Campus Rose Hill, Lincoln Center

Work Arrangement Hybrid

Hybrid Policy: Please review the Hybrid Policy at www.fordham.edu/hybridpolicy .

Position Summary Reporting to the Assistant Vice President of Enterprise AI, the AI/Machine Learning Engineer supports the advancement of the University’s enterprise AI strategy by designing, building, and implementing practical artificial intelligence and machine learning solutions across the university ecosystem. This senior role serves as a technical implementation lead, contributing to a dual-path approach focused on developing targeted AI agents to support the student journey and building predictive models that help anticipate institutional needs.

Working closely with the AVP , information technology, data governance, academic, and administrative stakeholders, the role translates institutional needs into secure, scalable, ethical, and privacy-compliant AI/ML solutions that improve campus operations, decision support, and student outcomes.

Essential Functions Designs and deploys multi-agent systems capable of reasoning, tool-use, and autonomous problem-solving throughout the student success lifecycle (Recruitment, Learning and Development, Career Services, and Alumni Relations).

Develops and maintains predictive models that provide actionable insights into areas such as student success, enrollment planning, operational efficiency, and early identification of students who may benefit from timely support.

Identifies opportunities to improve university operations and student outcomes through practical AI/ML solutions, including workflow automation, decision-support tools, and AI-assisted administrative processes.

Assists in the development of the technical roadmap for AI and machine learning implementation by assessing feasibility, documenting solution architecture, recommending scalable approaches, and supporting implementation priorities established by the AVP , with attention to ethics, privacy, security, FERPA / GDPR , and applicable governance standards.

Builds and supports retrieval-augmented generation workflows, vector database integrations, and AI application components using approved platforms, institutional data sources, and responsible development practices.

Serves as the liaison between university leadership (Administration, Deans, Faculty), technical AI/Data Science teams, and external vendors to align academic mission with technological execution.

Essential Functions Note **This list is not intended to be an exhaustive list.

The University may assign additional related duties as necessary.** Management Responsibilities Not responsible for supervision or oversight of others.

Additional Functions Architects AI agents using RAG (Retrieval-Augmented Generation) pipelines, Model Context Protocol, and Operational data.

Develops MLOps frameworks compliant with AI Governance policy.

Required Qualifications: Education and Experience Master’s in Computer Science, AI/Data science, Engineering, or a related field.

Minimum of 5 years of experience in Machine Learning or Data Science, with a track record of deploying predictive models in a production environment.

Required Qualifications: Knowledge and Skills Deep proficiency in Large Language Models (LLMs) and development & deployment of autonomous AI agents (fo…