Role: Data Bricks Infra Engineer
Location: Gurgaon (5 days mandatory work from office)
Type: Full-time
Experience: 8+ years
Key Responsibilities
- Design, build, and maintain secure connectors and pipelines between Databricks workspaces (Delta Lake, Unity Catalog) and Model Context Protocol (MCP) servers.
- Implement and configure MCP servers/clients to expose Databricks data, schemas, and analytical tools securely to AI models and LLM applications.
- Optimize data retrieval, caching mechanisms, and query performance between Databricks and LLM orchestration frameworks to minimize latency.
- Ensure all data exposed through the MCP server adheres to strict enterprise data governance, access controls, and Unity Catalog permissions.
- Partner with AI/ML engineers, data scientists, and software architects to define the context, tools, and prompts required for LLM applications to effectively query Databricks.
- Establish robust logging, error-handling, and monitoring for the Databricks-MCP middleware to ensure high availability and reliability.
Must-Have Skills
- Proven, hands-on experience building, configuring, or extending MCP servers (using Python or TypeScript/Node.js SDKs) to connect LLMs to external data sources.
- Deep production experience with Databricks (Delta Lake, Unity Catalog, Databricks SQL, and Managed MLflow
- Strong understanding of SSE (Server-Sent Events), WebSockets, and JSON-RPC 2.0 protocols, which underpin MCP communication
- Design, build, and maintain secure connectors and pipelines between Databricks workspaces (Delta Lake, Unity Catalog) and Model Context Protocol (MCP) servers.
- Implement and configure MCP servers/clients to expose Databricks data, schemas, and analytical tools securely to AI models and LLM applications.
- Optimize data retrieval, caching mechanisms, and query performance between Databricks and LLM orchestration frameworks to minimize latency.
- Ensure all data exposed through the MCP server adheres to strict enterprise data governance, access controls, and Unity Catalog permissions.
- Partner with AI/ML engineers, data scientists, and software architects to define the context, tools, and prompts required for LLM applications to effectively query Databricks.
- Establish robust logging, error-handling, and monitoring for the Databricks-MCP middleware to ensure high availability and reliability.
- Proven, hands-on experience building, configuring, or extending MCP servers (using Python or TypeScript/Node.js SDKs) to connect LLMs to external data sources.
- Deep production experience with Databricks (Delta Lake, Unity Catalog, Databricks SQL, and Managed MLflow
- Strong understanding of SSE (Server-Sent Events), WebSockets, and JSON-RPC 2.0 protocols, which underpin MCP communication