Dot Net AI Architect Lead

Project Role Description

As an AI Architect & .NET developer, you will be responsible for designing and governing end-to-end AI architectures on the Azure ecosystem that enables intelligent automation and decision support across insurance functions such as underwriting, claims, reinsurance, and document-heavy operations. The role focuses on building scalable, secure, and production-grade GenAI platforms leveraging LLMs, Agentic AI, OCR to process complex unstructured insurance documents (e.g., loss runs, policy forms, claims reports) and generate accurate, explainable, and auditable outputs. You will define architectural patterns, lead the implementation team, and partner with business and technology stakeholders to ensure AI solutions are enterprise-ready, cost-efficient, and aligned with regulatory and operational constraints.

Must Have Skills

1. GenAI Architecture
2. .NET (Backend) and React (Frontend) Developer
3. Azure AI / Azure AI Foundry experience / Vector Databases using Azure AI search
4. Prompt Engineering & LLM Design
5. Retrieval - Augmented Generation (RAG) Architectures

Good to Have Skills

1. Insurance Domain Knowledge (P&C / Commercial Lines / Reinsurance)
2. Agentic AI Frameworks (LangGraph, AutoGen, CrewAI, etc.)
3. OCR systems for document ingestion and classification
4. AI Governance & Token Economics

Role Summary

As an AI Architect & .NET developer, you will be responsible for designing and governing end-to-end AI architectures on the Azure ecosystem that enables intelligent automation and decision support across insurance functions such as underwriting, claims, reinsurance, and document-heavy operations.

The role focuses on building scalable, secure, and production-grade GenAI platforms leveraging LLMs, Agentic AI, OCR to process complex unstructured insurance documents (e.g., loss runs, policy forms, claims reports, bordereaux) and generate accurate, explainable, and auditable outputs. You will define architectural patterns, lead the implementation team, and partner with business and technology stakeholders to ensure AI solutions are enterprise-ready, cost-efficient, and aligned with regulatory and operational constraints.

Key Responsibilities

Architecture & Solution Design

• Act as an AI Architect and SME for GenAI-driven insurance use cases
• Define end-to-end AI architecture for unstructured document ingestion, reasoning, and output generation
• Design LLM-centric and hybrid AI architectures combining:
  • OCR
  • RAG systems
  • Agentic workflows

GenAI & Prompt Architecture

• Design and govern prompt strategies and prompt frameworks for:
  • Loss run and insurance document extraction & normalization
  • Claims summarization, triage, and fraud signal generation
  • Underwriting risk assessment and decision support
• Establish prompt versioning, testing, and optimization standards for enterprise use

Agentic AI & Workflow Orchestration

• Architect Agentic AI systems for multi-step reasoning, task decomposition, and tool orchestration
• Define patterns for human-in-the-loop, approvals, and exception handling
• Drive adoption of agent orchestration frameworks (LangGraph, AutoGen, CrewAI) in production scenarios

RAG & Knowledge Architecture

• Design RAG-based knowledge architectures for policy, claims, and underwriting data
• Define chunking, embedding, retrieval, and grounding strategies
• Ensure traceability and explainability of generated outputs

Enterprise & Platform Architecture - Azure

• Drive architectural decisions related to:
  • Scalability and performance
  • Cost optimization of LLM usage
  • Security, data privacy, and access control
  • Auditability and regulatory compliance
• Define reference architectures and reusable components for multiple insurance use cases

Evaluation, Quality & Optimization

• Establish evaluation frameworks for GenAI solutions, including:
  • Precision, recall, and F1 metrics
  • Grounding and hallucination detection
  • Consistency and explainability checks

Collaboration & Leadership

• Partner with business stakeholders (Underwriting, Claims, Actuarial, Legal) to shape AI roadmaps
• Technical project lead experience
• Guide and mentor .NET developers, React developers, and GenAI developers
• Define best practices, standards, and architectural guardrails for GenAI adoption

Technical Stack & Platform Experience

Programming & Frameworks

• Strong proficiency in .NET/React

GenAI & LLM Platforms

• Azure OpenAI APIs / enterprise LLM platforms

Architecture & Integration

• API-first design
• Microservices-based architectures
• Experience integrating AI solutions into enterprise systems

This document is confidential and intended only for the use of the individual or entity to which it is addressed.