AI Platform Engineer and Advocate (Databricks)

1. Key Responsibilities

Engage Hands-On

• Evaluate new Databricks features and GenAI tools.
• Recommend which technologies to adopt, wait for, or avoid.
• Lead Databricks platform engagements with business teams.
• Provide technical consulting, blueprints, and best practices.
• Convert hands-on project learnings into reusable solutions and patterns.

Define Standards & Documentation

• Establish guardrails and standards to ensure platform scalability and governance.
• Develop reference architectures, technical blueprints, and implementation guides.
• Create technically sound and easy-to-follow best-practice documentation.
• Review Databricks releases and provide internal recommendations on relevant features.

Build Technical Communities

• Establish and lead a community of practice for Data Scientists and ML Engineers.
• Conduct technical training sessions, hands-on workshops, and hackathons.
• Organize technical demonstrations, office hours, and knowledge-sharing programs.
• Present at internal technology forums and communicate platform strategy to senior leadership.

Drive Adoption & Continuous Improvement

• Identify technical challenges and adoption blockers faced by teams.
• Share prioritized feedback with the platform engineering team.
• Track platform adoption and engineering practice maturity.
• Report progress and encourage consistent implementation practices across business units.

2. Mandatory Skills & Experience

Experience

• 6+ years of experience in Data Science, Machine Learning Engineering, or AI Platform roles.
• Hands-on experience building machine learning models or AI systems.
• Experience defining MLOps or engineering standards adopted by multiple teams.

Databricks Platform Experience

• Production-level hands-on experience with:

• Unity Catalog

• MLflow

• Model Serving

• Lakeflow Jobs / Workflows

Advanced Databricks Capabilities

• Experience with newer Databricks features, including:

• Agent Bricks

• Genie

• Feature Engineering

• AI Gateway

Generative AI & MLOps

• Hands-on experience developing GenAI applications, including:

• Retrieval-Augmented Generation (RAG)

• AI Agents

• GenAI evaluation

• Monitoring of AI applications

Communication & Leadership

• Ability to explain complex technical concepts to engineers, data scientists, and senior leadership.
• Strong consulting, presentation, and stakeholder-management skills.
• Experience conducting technical workshops and knowledge-sharing sessions.
• Ability to influence teams and drive the adoption of common engineering standards.