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.

1. 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.
- Robust 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.

📌 AI Platform Engineer and Advocate (Databricks) (Bengaluru)
🏢 svan global consultancy
📍 Bengaluru