Databricks AI Architect
Tasks
- Apply MLOps and LLMOps practices
- Build GenAI pipelines and production workflows
- Design AI agents and multi agent workflows
- Design Databricks Lakehouse architectures
- Develop enterprise data pipelines
- Embed RAG and vector search
- Implement CI/CD pipelines
- Integrate AI assistants into data platforms
- Integrate Azure AI services with Databricks
- Monitor optimize platform performance and cost
- Unify data analytics and AI
- Use infrastructure as code
Perks/Benefits
Skills/Tech-stack
AI Agents | AI Services | Agent Orchestration | Azure AI | Azure AI Services | CI/CD | Databricks | Embeddings | GenAI | GitHub Copilot | Infrastructure as Code | LLMOps | Lakehouse | Language Models | Large Language Models | MLOps | MLflow | Multi-Agent | Multi-agent orchestration | RAG | Retrieval-Augmented Generation | Unity Catalog | VS Code | Vector Databases | Vector Search | “as-code”
Education
N/A
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