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Fordham University Portal
AI/Machine Learning Engineer
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What they do
A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
$123,933 / year median in Virginia
Job Description
AI/Machine Learning Engineer Full Time Rose Hill, VA, United States Posted 6 days ago
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.
Requirements Designing, building, and implementing practical artificial intelligence and machine learning solutions.
Technical implementation lead experience.
Developing targeted AI agents to support the student journey.
Building predictive models to anticipate institutional needs.
Translating institutional needs into secure, scalable, ethical, and privacy-compliant AI/ML solutions.
Improving campus operations, decision support, and student outcomes through AI/ML.Designing and deploying multi-agent systems.
Developing and maintaining predictive models.
Identifying opportunities for AI/ML solutions.
Assisting in the development of technical roadmaps for AI/ML implementation.
Assessing feasibility, documenting solution architecture, recommending scalable approaches.
Building and supporting retrieval-augmented generation workflows.
Building and supporting vector database integrations.
Building and supporting AI application components.
Using approved platforms, institutional data sources, and responsible development practices.
Serving as a liaison between university leadership, technical teams, and external vendors.
Nice-to-haves Ph.D degree in Computer Science, Engineering, or a related field.
Closes:
Nov 1, 2026 Fordham University Portal Category Software Engineers/Developers Tags Academia Machine Learning NLP United States Share This Click to share on LinkedIn (Opens in new window) Click to share on Facebook (Opens in new window) Click to share on Twitter (Opens in new window) More Click to share on Reddit (Opens in new window) Click to share on Pinterest (Opens in new window) Click to share on Tumblr (Opens in new window) Click to share on Pocket (Opens in new window) Apply for job Login to bookmark this Job Overview About the position 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. Responsibilities 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.
Requirements Designing, building, and implementing practical artificial intelligence and machine learning solutions.
Technical implementation lead experience.
Developing targeted AI agents to support the student journey.
Building predictive models to anticipate institutional needs.
Translating institutional needs into secure, scalable, ethical, and privacy-compliant AI/ML solutions.
Improving campus operations, decision support, and student outcomes through AI/ML.Designing and deploying multi-agent systems.
Developing and maintaining predictive models.
Identifying opportunities for AI/ML solutions.
Assisting in the development of technical roadmaps for AI/ML implementation.
Assessing feasibility, documenting solution architecture, recommending scalable approaches.
Building and supporting retrieval-augmented generation workflows.
Building and supporting vector database integrations.
Building and supporting AI application components.
Using approved platforms, institutional data sources, and responsible development practices.
Serving as a liaison between university leadership, technical teams, and external vendors.
Nice-to-haves Ph.D degree in Computer Science, Engineering, or a related field.