Applied AI Engineer, Learning Intelligence

Databricks Databricks · Data AI · CA · Remote · Education & Training

This role focuses on building the intelligence layer for learner growth, involving ML models, knowledge representation, and product engineering. The engineer will own the skill and concept graph, infer skill gaps, and build recommendation systems. They will also ensure AI-driven features are explainable, reliable, and production-ready, partnering with frontend engineers. Experience with LLM APIs, prompt engineering, and shipping LLM-based systems to production is required.

What you'd actually do

  1. Design, build, and maintain a skill and concept graph that maps relationships between skills, roles, domains, and learning content
  2. Develop ML models that infer learner skill levels from usage patterns, work output, assessments, and profile data (not just self-reported input)
  3. Build and iterate on recommendation systems that surface the next best module, suggest learning paths, and generate content dynamically
  4. Partner with frontend engineers to ensure AI outputs are consumed correctly, surfaced with appropriate context
  5. Define explainability standards for model outputs so users and stakeholders understand why a recommendation was made

Skills

Required

  • 5+ years of experience in applied ML or data science
  • production recommendation or personalization systems
  • knowledge graphs, graph databases, or ontology design
  • LLM APIs and prompt engineering
  • shipping LLM-based systems to production
  • large-scale deployment
  • evaluation frameworks
  • agentic workflows
  • Advanced Python proficiency
  • architecting robust, production-grade applications
  • modern AI stack
  • retrieval and agent frameworks
  • complex prompt engineering
  • model evaluation
  • context engineering
  • communication skills

Nice to have

  • intellectual curiosity
  • ability to find elegant, straightforward solutions
  • translate technical logic for varied stakeholders

What the JD emphasized

  • production recommendation or personalization systems
  • shipping LLM-based systems to production
  • evaluation frameworks
  • agentic workflows

Other signals

  • shipping LLM-based systems to production
  • production recommendation or personalization systems
  • skill and concept graph
  • ML models that infer learner skill levels
  • recommendation systems