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Agentic AI Developer
Posted via LinkedIn Recruiter (not a company profile)
Posted yesterday
About the role
- Contract Agentic AI Developer role. Develop AI agents using LangGraph/LangChain, design SQL agents for Snowflake, build MCP integrations, and implement RAG patterns.
Responsibilities
- Develop and integrate new domain-specific AI agents into an existing multi-agent orchestration system built on LangGraph and LangChain. Design and implement SQL generation agents that translate natural language questions into precise Snowflake SQL, enforcing business rules, RBAC, and fiscal period logic. Build and consume MCP (Model Context Protocol) server integrations for secure, structured data access across enterprise data sources. Work with PromptQL and RAG/PageIndex patterns to improve query accuracy, context retrieval, and domain-specific grounding. Develop FastAPI endpoints and async workflows to support real-time query processing, background job execution, and frontend integration. Create intelligent data visualization pipelines that automatically select and generate the right chart type (heatmaps, bar charts, KPIs) based on query results and user intent. Write domain context configurations (JSON schemas) that define column mappings, business rules, valid values, and metric definitions for each data domain. Contribute to a plugin-based domain registry architecture that allows new agents to be added without modifying core orchestration code.
Requirements
- Mandatory skills: Python – Expert-level proficiency; ability to write production-quality async code, work with complex class hierarchies, and debug multi-layer systems. Snowflake – Strong experience writing and optimizing analytical SQL; understanding of RBAC patterns, fiscal period logic, and aggregation queries. LangGraph / LangChain – Hands-on experience building stateful, multi-step AI agent workflows with tool calling, checkpointing, and conditional routing. RAG / PageIndex – Practical experience implementing retrieval-augmented generation pipelines for context-aware AI applications. MCP (Model Context Protocol) – Practical experience with MCP client/server patterns for structured tool-to-data communication. Snowflake Cortex – Experience with Snowflake Cortex AI functions (COMPLETE, EXTRACT_ANSWER, SUMMARIZE, SENTIMENT) and building AI/ML workflows natively within Snowflake. PromptQL – Experience with structured prompt engineering and query language patterns for grounding LLM outputs in enterprise data.
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