Applied Artificial Intelligence Engineer

Vanderbilt University•Nashville, TN

3h•Remote

About The Position

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 Vanderbilt's 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 we're ready to operationalize. We're seeking an AI Engineer to help us realize our vision for precision medical education—using data and AI to personalize learning, predict learner needs, and improve educational outcomes at scale. You'll 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, you'll also contribute to predictive modeling efforts, including early identification of at-risk learners and competency progression analytics. This is a high-ownership, foundational role. You'll shape AI strategy and implementation from the ground up, taking features from concept through production deployment. You'll 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. 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 VUSM’s 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 outcomes—empowering them to advocate for positive change in both local and global communities.

Requirements

A Bachelor’s degree in a related field from an accredited institution of higher education is necessary.
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.
Demonstrated self-direction and ownership mentality in previous roles is necessary.

Nice To Haves

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.

Responsibilities

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
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
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 EDI's 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