Applied Artificial Intelligence Engineer

*This position does not provide sponsorship for employment now or in the future in any format.

Position Summary:
The Applied Artificial Intelligence Engineer is part of the Education Design and Informatics team within the Office of Health Sciences Education at Vanderbilt University School of Medicine. The Education Design & Informatics Team (EDI) is responsible for technology instructional design data infrastructure and quality improvement for Vanderbilts MD program and affiliated health professions education. We maintain a comprehensive education data lake in Databricks a custom digital learning platform (VSTAR) and a vision for AI-augmented medical education that were ready to operationalize.

Were seeking an AI Engineer to help us realize our vision for precision medical educationusing data and AI to personalize learning predict learner needs and improve educational outcomes at scale.

Youll be our first dedicated AI/ML role working alongside a data engineer full-stack developers and instructional designers to build intelligent features into our products. Near-term priorities include LLM-powered tools for learners and educators semantic search across educational content recommendation systems for personalized learning pathways and integration of AI capabilities into VSTAR. As our AI maturity grows youll also contribute to predictive modeling efforts including early identification of at-risk learners and competency progression analytics.

This is a high-ownership foundational role. Youll shape AI strategy and implementation from the ground up taking features from concept through production deployment. Youll work closely with educational leadership
to identify opportunities where AI can meaningfully improve how we train future physicians.

This position is fully remote and reports directly to the Director of Education Design & Informatics. Open to hybrid availability if interested.

About the Work Unit:
The Office of Health Sciences Education (OHSE) administers the degree programs of the Vanderbilt University
School of Medicine (VUSM) and supports its mission to catalyze impactful discovery servant leadership and
lifelong learning. OHSE staff provide comprehensive support across academic program administration
educational design and informatics student services and financial and operational oversight.

In alignment with VUSMs commitment to excellence OHSE works to prepare future leaders in the health
professions who are equipped with the knowledge skills and judgment needed to deliver safe effective ethical and patient-centered care. Our students are encouraged to think critically foster innovation and understand the broader systems and social factors that shape health outcomesempowering them to advocate for positive change in both local and global communities.

Key Functions and Expected Performance:
AI Application Development

Design and build AI-powered features for VSTAR and other EDI platforms including intelligent
tutoring capabilities semantic search content recommendations and LLM-based tools for learners
and educators
Apply appropriate AI implementation patterns and strategies such as RAG architectures
agentic workflows prompt engineering strategies and LLM orchestration patterns appropriate to
educational use cases
Develop backend services and APIs that expose AI capabilities for integration into VSTAR and
other applications working with the development team to determine appropriate integration patterns
Evaluate vender versus open-source AI products and services based on performance cost and
reliability considerations
Ensure responsible AI practices including appropriate guardrails content filtering and
transparency in AI- assisted features

ML Engineering & Production Systems
Build and maintain ML pipelines in Databricks for feature engineering model training and
evaluation
Deploy models and AI services to production with appropriate monitoring logging and error
handling
Implement MLOps practices proportionate to our maturity: version control testing
documentation and reproducibility
Ensure performance reliability and scalability of AI-powered services
Own the full lifecycle of deployed AI features including maintenance iteration and
retirement
Contribute to predictive modeling initiatives addressing educational challenges such as
learner performance prediction early intervention identification and resource optimization

Collaboration & Strategy
Partner with data engineering to ensure AI systems integrate cleanly with our data
infrastructure
Collaborate with software developers to integrate AI features into existing applications
Proactively communicate progress challenges and decisions to the team through regular
check-ins documentation and asynchronous updates
Work with product and educational leadership to identify high-impact AI opportunities
Contribute to EDIs AI strategy and help establish best practices for responsible AI
development in medical education
Maintain clear documentation and support knowledge sharing across the team
Stay current with developments in AI tooling particularly as they apply to education and
knowledge work

Education and Certifications:
A Bachelors degree in a related field from an accredited institution of higher education is
necessary.

Experience and Skills:
5 7 years of experience i s required.
Experience in applied machine learning AI engineering or a related field (3 years) is
necessary.
Strong Python skills and experience with ML frameworks such as scikit-learn PyTorch or
TensorFlow (3 years) i s necessary.
Hands-on experience building applications with LLMs including prompt engineering
embeddings retrieval-augmented generation and agents (1 years) is necessary.
Experience developing backend services (FastAPI Flask or similar) and RESTful APIs (1
years) is necessary.
Track record of deploying AI or ML features to production environments (1 years) is
necessary.
Comfort with SQL and working with data pipelines (3 years) is necessary.
Ability to communicate technical concepts clearly to non-technical audiences (3 years) is
necessary.
Experience with Databricks and Azure cloud services (1 years) is preferred.
Familiarity with MLOps tools and practices (MLflow model registries CI/CD for ML) (1
years) is preferred.
Experience with vector databases (Pinecone Weaviate Chroma or similar) (1 years) is
preferred.
Experience working with multiple LLM providers or open source LLMs and evaluating tradeoffs
(1 years) is preferred.
Background in building predictive models (classification regression forecasting) (1 years)
is preferred.
Experience in education healthcare or other mission-driven sectors (1 years) is preferred.
Familiarity with the unique considerations of AI in educational contexts (pedagogical
alignment learner privacy appropriate automation) (1 years) is preferred.
Demonstrated self-direction and ownership mentality in previous roles is necessary.