• Evaluate new Databricks features and GenAI tooling, and give clear adopt / wait / avoid
recommendations
• Lead Databricks platform engagements with Business Area teams by providing consulting,
blueprints, and best practices
• Turn what you learn in engagements into reusable patterns for everyone else
Write the playbook
• Produce reference architecture, blueprints, and step-by-step implementation guides that teams use
• Write opinionated best-practice guides that are technically rigorous and easy to use
• Turning Databricks releases into clear internal guidance on what matters for us, what to adopt, and
what to ignore
• Build and lead a community of practice for data scientists and ML engineers across all Business
Areas
• Design and delivery training, hands-on workshops, office hours, and hackathons
• Present at internal tech forums and explain the platform vision to senior leadership
Close the loop
• Collect pain points and adoption blockers from teams, and turn them into prioritized input for the
platform team
• Measure adoption and practice maturity, and report on progress
Must-haves
• 6+ years in data science, ML engineering, or AI platform roles, including time spent building models
or AI systems yourself
• Hands-on Databricks experience in production: Unity Catalog, MLflow, Model Serving, and Lakeflow
Jobs/Workflows
• Experience with newer Databricks capabilities such as Agent Bricks, Genie, Feature Engineering,
and AI Gateway
• Hands-on experience building GenAI applications such as RAG or agents, including evaluation and
monitoring
• Experience defining MLOps or engineering standards that other teams adopted
• You explain complex topics clearly to engineers, data scientists, and leadership, and people enjoy
your workshops
• Build and lead a community of practice for data scientists and ML engineers across all Business
Areas
• Design and delivery training, hands-on workshops, office hours, and hackathons
• Present at internal tech forums and explain the platform vision to senior leadership
Close the loop
• Collect pain points and adoption blockers from teams, and turn them into prioritized input for the
platform team
• Measure adoption and practice maturity, and report on progress
Must-haves
• 6+ years in data science, ML engineering, or AI platform roles, including time spent building models
or AI systems yourself
• Hands-on Databricks experience in production: Unity Catalog, MLflow, Model Serving, and Lakeflow
Jobs/Workflows
• Experience with newer Databricks capabilities such as Agent Bricks, Genie, Feature Engineering,
and AI Gateway
• Hands-on experience building GenAI applications such as RAG or agents, including evaluation and
monitoring
• Experience defining MLOps or engineering standards that other teams adopted
• You explain complex topics clearly to engineers, data scientists, and leadership, and people enjoy
your workshops
