Applied AI Engineer, Learning Intelligence & Personalization

Neura Market; Northern (KY); Hybrid; USD 111,000 - 191,000; Full time

Job summary

Databricks is seeking an Applied AI Engineer, Learning Intelligence to design and build the knowledge graph that powers personalized learning journeys. You will bridge ML and product engineering, ensuring explainable, reliable AI features ship to users.

You'll develop ML models to infer skill levels from usage, create scalable recommendation systems, and define evaluation frameworks for production deployments, collaborating with frontend and content teams.

Qualifications

• 5+ years of experience in applied ML or data science with production recommendations or personalization.
• Experience with knowledge graphs, graph databases, or ontology design.
• Hands-on history shipping LLM-based systems to production and large-scale deployment.
• Advanced Python proficiency and robust production-grade applications.

Responsibilities

• Design, build, and maintain a skill and concept graph that maps relationships between skills, roles, domains, and learning content.
• Develop ML models to infer learner skill levels from usage patterns, work output, assessments, and profile data.
• Build and iterate on recommendation systems that surface next best modules and learning paths.
• Partner with frontend engineers to ensure AI outputs are consumed correctly with context.
• Define explainability standards for model outputs for transparency.
• Collaborate with product and content teams to validate recommendation quality and close feedback loops.
• Monitor model performance and own the evaluation framework for recommendations.

Skills

Applied ML

Python

Knowledge graphs

LLM prompts

Production systems

Communication

Prompt engineering

System design

Tools

Graph databases

Ontology design

LLM deployment

Job description

Databricks is seeking an Applied AI Engineer, Learning Intelligence to design and build the knowledge graph that powers personalized learning journeys. You will bridge ML and product engineering, ensuring explainable, reliable AI features ship to users.

You'll develop ML models to infer skill levels from usage, create scalable recommendation systems, and define evaluation frameworks for production deployments, collaborating with frontend and content teams.