Applied Artificial Intelligence Engineer

Role Overview

• 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
• 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

Requirements

• 5 – 7 years of experience is 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) is 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.

Tech Stack

• Azure
• Cloud
• Flask
• Open Source
• Python
• PyTorch
• Scikit-Learn
• SQL
• Tensorflow

Benefits

• Health insurance
• Professional development opportunities
• Flexible work arrangements