Lead Assistant Manager
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
Graduate in Computer Science, Data Science, or related field. 8+ years of experience in data engineering or a related field.
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.
