GenAI Architect

Key Responsibilities

As a Generative AI Architect, you will :

• Design and Implement Intelligent Agents : Lead the architecture, development, and deployment of sophisticated multi-step intelligent agents using LangGraph for complex workflows.
• Integrate and Optimize AI Tools : Leverage MCP tools effectively within agent designs to enhance functionality and performance.
• Cloud-Native Deployment : Implement and manage agent deployments on Azure AI Foundry and Functions , ensuring scalable, robust, and efficient operations.
• RAG Stack Optimization : Work with the broader retrieval-augmented generation (RAG) stack, including embeddings, vector databases, and chunking strategies , to enable intelligent document understanding across insurance submissions and claims.
• Agent Orchestration & Debugging : Comfortably engineer prompts, orchestrate agent interactions, and meticulously debug complex multi-step agent behaviors.
• Develop Specific Agent Use Cases :
• Submissions Agent : Build agents to parse submission emails and documents, extract critical data, apply underwriting processing and knowledge, and prioritize tasks with full transparency and repeatability.
• Bordereaux Reconciliation Agent : Automate the matching of premium and policy data across bordereaux files and internal systems.
• Claims Notification Agent : Develop agents to ingest claim notices and surface critical items requiring human intervention.
• Supervisor Agent : Design and implement a supervisor agent to coordinate graph-based task execution and ensure secure data handling per client.
• Bordereaux Extraction Agent : Work with unstructured data, vision processing, and LLMs for accurate data extraction.

Required Skills & Experience

Bonus Qualifications

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Skills Required

LangChain, LangGraph, vector stores, embeddings, MCP Tools, agent orchestration, LangSmith, Azure AI Foundry, RAG architectures, prompt engineering