About this role
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