Role: AI Engineer (RAG, LangGraph Agents, MCP, Databricks Genie, Snowflake Cortex) – Mid Level

Key Responsibilities

• Design and implement RAG pipelines end-to-end, including document ingestion, chunking, embedding, retrieval, and response generation for well-scoped product features or internal tools.
• Build and maintain LangGraph-based agents, modeling workflows as graphs (nodes, edges, conditional branches, loops) and integrating tools, retrievers, and external APIs.
• Implement and operate MCP servers that expose tools/resources to LLM hosts, including request/response handling, basic error handling, and logging.
• Integrate with LLM providers (e.g., Azure OpenAI, Databricks) and manage prompts, model configurations, and context windows for RAG-style interactions.
• Work with vector stores (e.g., Azure AI Search) to set up indexes, tune similarity search, and manage metadata for retrieval.
• Contribute to data ingestion pipelines for RAG (file connectors, text extraction, cleaning, metadata enrichment) in collaboration with data/platform teams.
• Build and productionize GenAI and RAG-style workloads on Databricks, leveraging tools such as Databricks Genie / AI Assistant for SQL, notebooks, and pipeline development, and integrating Databricks-native vector search or ML flows where appropriate.
• Design and implement AI-powered analytics and applications on Snowflake, using Snowflake Cortex capabilities (built-in LLM functions, embeddings, and vector search) as part of RAG and agent architectures.
• Add observability to agents, RAG components, and MCP servers (logging, tracing, metrics), and use these to debug failures, latency issues, and incorrect tool usage.
• Collaborate with product managers and senior engineers to translate requirements into technical designs and incremental delivery plans.
• Participate in code reviews, testing, and documentation, improving reliability, maintainability, and clarity of RAG, LangGraph, Databricks, and Snowflake-related codebases.
• Support security and compliance efforts by following established best practices around authentication, authorization, secrets management, and safe tool usage across cloud platforms.